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# AnalystAIPack — GitHub Copilot instructions
This workspace is **AnalystAIPack**, a library of 118 security skills for **malware analysis,
reverse engineering, and threat hunting**. Each skill is a folder under `skills/` containing a
`SKILL.md` (procedure) and a runnable `scripts/analyst.py` (a static, read-only analysis tool).
## How to help in this repo
- When the user asks an analysis/hunting task, **find the matching skill first** instead of
improvising. Discover skills with:
- `python tools/analyst-pack.py search <topic>`
- `python tools/analyst-pack.py list [--subdomain malware-analysis|reverse-engineering|threat-hunting|lab-foundations]`
- `python tools/analyst-pack.py show <skill-name>`
- Read the chosen `skills/<name>/SKILL.md` and follow its **Workflow**. Honor its
`When to Use` and especially the **Do not use** boundary.
- Run a skill's tool via:
`python tools/analyst-pack.py run <skill-name> -- <subcommand> <args>`
or directly `python skills/<skill-name>/scripts/analyst.py <subcommand> <args>`.
Output is structured JSON — pass it forward to the next skill or into a report.
## Safety rules (do not violate)
- **Never execute a malware sample.** All scripts are static and read-only. Do not write code
that runs, detonates, or connects to a sample's infrastructure.
- **Assume an isolated lab** for any live-sample step; follow each skill's `Safety & Handling`.
- **Keep IOCs defanged** in output (`hxxp://`, `1[.]2[.]3[.]4`). The scripts already do this —
preserve it.
- **No live samples in the repo** (see `.gitignore`). Some scripts contain malware *signature
strings* for detection; antivirus may flag the file text — that is static data, not behavior.
## Authoring or editing a skill
Keep the repo green and follow the conventions:
- Each skill needs: `SKILL.md` + `scripts/analyst.py` + `references/api-reference.md` + `LICENSE`.
- Frontmatter: `name` (kebab == folder), `description` (ends with an "Activates for …" trigger),
`domain: cybersecurity`, `subdomain` (one of the four), `tags`, `version`, `author`, `license`,
and framework IDs (`mitre_attack`, `d3fend`, optional `car`).
- Body sections in order: `When to Use` (with **Do not use**), `Prerequisites`,
`Safety & Handling` (sample-handling skills), `Workflow`, `Validation`, `Pitfalls`, `References`.
- Scripts are **standard-library only** (optional deps degrade gracefully) and never execute the
sample. Validate with:
```bash
python tools/lint-skills.py --all
python tools/build-index.py && python tools/build-catalog.py && python tools/build-navigator-layer.py
python tools/smoke-test.py
```
See `AGENTS.md` (full operating guide) and `CONTRIBUTING.md` (originality rules).

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name: ci
on:
push:
branches: [main, master]
pull_request:
workflow_dispatch:
permissions:
contents: read
jobs:
validate:
name: Validate, build artifacts, and smoke-test
runs-on: ubuntu-latest
steps:
- name: Check out repository
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.12"
# Core runtime is stdlib-only; no dependency install step is required.
- name: Lint all skills
run: python tools/lint-skills.py --all
- name: Verify index.json is current
run: python tools/build-index.py --check
- name: Verify ATT&CK Navigator layer is current
run: python tools/build-navigator-layer.py --check
- name: Verify CATALOG.md is current
run: python tools/build-catalog.py --check
- name: Byte-compile every skill script
run: |
python -m compileall -q skills tools
- name: Smoke-test all skill scripts
run: python tools/smoke-test.py
- name: Smoke-test the analyst-pack CLI
run: |
python tools/analyst-pack.py stats
python tools/analyst-pack.py list

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# Python
__pycache__/
*.py[cod]
*$py.class
*.egg-info/
.eggs/
build/
dist/
.venv/
venv/
env/
# Tooling / caches
.ruff_cache/
.mypy_cache/
.pytest_cache/
.coverage
htmlcov/
# Editor / OS
.vscode/
.idea/
*.swp
.DS_Store
Thumbs.db
# Analyst working data — never commit live samples or local sandboxes
# (AnalystAIPack ships no malware; keep captured specimens out of the tree)
samples/
*.sample
*.malware
*.dmp
*.bin.enc
quarantine/
# Local-only demo tooling, fixtures, and recordings (kept out of the tree)
demo/

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# AGENTS.md — Using AnalystAIPack as an AI agent
This repository is a **skill library** for malware analysis, reverse engineering, and threat
hunting. If you are an AI coding/security agent, read this file first: it tells you how to
find the right skill, run it safely, and chain skills into an investigation.
## What this repo is
- 118 self-contained **skills** under [`skills/`](skills/), one folder each.
- Every skill has: `SKILL.md` (the procedure), `scripts/analyst.py` (a runnable tool),
`references/api-reference.md` (script docs + sources), and `LICENSE`.
- Machine-readable catalog: [`index.json`](index.json). Human catalog: [`CATALOG.md`](CATALOG.md).
## How to pick a skill
1. Search the catalog by intent, not just keyword:
```bash
python tools/analyst-pack.py search <topic> # e.g. "kerberos", "pe imports", "beacon"
python tools/analyst-pack.py list --subdomain threat-hunting
```
2. Read the chosen `SKILL.md`. **Honor its `When to Use` and especially the `**Do not use**`
line** — it states the boundaries of the technique. Do not apply a skill outside its scope.
## How to run a skill
Each skill's tool is a small CLI. Inspect it, then run it:
```bash
python tools/analyst-pack.py show <skill-name> # see its subcommands
python tools/analyst-pack.py run <skill-name> -- <args> # run it
# or directly:
python skills/<skill-name>/scripts/analyst.py <subcommand> <args>
```
Scripts print **structured JSON**. Use that output as input to the next skill or a report.
## Safety rules (do not violate)
- **Never execute the malware sample.** Every script performs *static, read-only* analysis.
Do not add code that runs, detonates, or connects to a sample's infrastructure.
- **Assume an isolated lab.** Skills that handle samples include a `Safety & Handling`
section — follow it. Do not move samples out of the working directory.
- **Keep IOCs defanged** in any output you share (`hxxp://`, `1[.]2[.]3[.]4`). The scripts
already do this; preserve it.
- **No live samples in the repo.** This project ships none; never commit one (see
[`.gitignore`](.gitignore)).
## Chaining skills (the analyst loop)
A full investigation usually flows: **triage → static analysis → unpack/reverse → extract
config & IOCs → defang → hunt across telemetry → write a detection.** See the worked example
in [`README.md`](README.md#a-worked-example-from-sample-to-detection). Pass each script's JSON
output forward; defang before sharing.
## Framework mapping
Skill frontmatter carries `mitre_attack`, `d3fend`, and (for hunts) `car` IDs. Use these to
report coverage and to slot findings into detection engineering. The ATT&CK Navigator layer is
at [`mappings/attack-navigator-layer.json`](mappings/attack-navigator-layer.json).
## If you add or edit a skill
Keep the repo green — run before finishing:
```bash
python tools/lint-skills.py --all # schema + body-section linter
python tools/build-index.py # regenerate index.json
python tools/build-catalog.py # regenerate CATALOG.md
python tools/build-navigator-layer.py # regenerate the ATT&CK layer
python tools/smoke-test.py # run every analyst.py (--help + fixtures)
```
Authoring rules (originality, sourcing, safety) are in [`CONTRIBUTING.md`](CONTRIBUTING.md).

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# AnalystAIPack Skill Catalog
Auto-generated index of all **118** skills. Regenerate with `python tools/build-catalog.py`.
| Subdomain | Skills |
|---|---|
| [Lab Foundations](#lab-foundations) | 12 |
| [Malware Analysis](#malware-analysis) | 38 |
| [Reverse Engineering](#reverse-engineering) | 35 |
| [Threat Hunting](#threat-hunting) | 33 |
## Lab Foundations
12 skills.
| Skill | What it does | ATT&CK |
|---|---|---|
| [`building-a-sample-management-workflow`](skills/building-a-sample-management-workflow/SKILL.md) | Establishes a disciplined malware sample repository: content-addressed storage by hash, encrypted/password-protected archiving, consistent metadata records, and chain-of-custody tracking so samples are reproducible and safe to handle. | T1587 |
| [`collecting-volatile-evidence-from-a-suspect-host`](skills/collecting-volatile-evidence-from-a-suspect-host/SKILL.md) | Collects volatile evidence from a potentially compromised host in correct order of volatility: memory, network state, running processes, logged-on users, and open handles, while preserving integrity and minimizing footprint. | T1057, T1049 |
| [`defanging-and-sharing-iocs`](skills/defanging-and-sharing-iocs/SKILL.md) | Prepares indicators of compromise for safe sharing: defanging URLs, domains, IPs, and emails so they cannot be accidentally clicked or resolved, and formatting them into STIX/CSV/MISP-friendly output. | T1071 |
| [`enriching-iocs-with-threat-intel-sources`](skills/enriching-iocs-with-threat-intel-sources/SKILL.md) | Enriches indicators with context from threat-intel sources: planning lookups against reputation, passive DNS, WHOIS, and sandbox/sample databases, scoring confidence, and avoiding operational-security mistakes that tip off adversaries. | T1583, T1071 |
| [`extracting-iocs-from-analysis-output`](skills/extracting-iocs-from-analysis-output/SKILL.md) | Extracts indicators of compromise from raw analysis artifacts: parsing strings dumps, sandbox reports, PCAP summaries, and logs for URLs, domains, IPs, hashes, mutexes, and file paths, then deduplicating and typing them. | T1071, T1583 |
| [`handling-malware-samples-safely`](skills/handling-malware-samples-safely/SKILL.md) | Establishes safe practices for acquiring, storing, transferring, and disposing of malware samples: password-protected archives, neutralized extensions, hashing for identity, and chain-of-custody. | T1027 |
| [`hashing-and-fingerprinting-files`](skills/hashing-and-fingerprinting-files/SKILL.md) | Computes cryptographic and fuzzy fingerprints for malware samples: MD5/SHA-1/SHA-256 for exact identity, plus import hashing (imphash), section hashing, and fuzzy hashes (ssdeep, TLSH) to cluster related variants. | T1027 |
| [`identifying-file-types-and-formats`](skills/identifying-file-types-and-formats/SKILL.md) | Identifies a sample's true file type independent of its extension: matching magic bytes and structural signatures, detecting container/archive formats, and flagging extension-content mismatches used to disguise malware. | T1036.008, T1036 |
| [`scoping-an-incident-from-a-single-indicator`](skills/scoping-an-incident-from-a-single-indicator/SKILL.md) | Expands one indicator into the full scope of an incident: pivoting across data sources to find related hosts, accounts, and infrastructure, building a timeline, and bounding what is and is not affected. | T1071, T1078 |
| [`setting-up-an-isolated-malware-analysis-lab`](skills/setting-up-an-isolated-malware-analysis-lab/SKILL.md) | Builds a safe, isolated environment for detonating and analyzing malware using virtualization, host-only or simulated networking, and snapshotting. | T1564, T1497 |
| [`triaging-an-unknown-sample`](skills/triaging-an-unknown-sample/SKILL.md) | Performs fast first-pass triage of an unknown file to decide depth of analysis: file type identification, hashing, reputation lookup, packing/entropy check, and string review. | T1027, T1140 |
| [`writing-a-malware-analysis-report`](skills/writing-a-malware-analysis-report/SKILL.md) | Structures a clear, actionable malware analysis report covering summary, sample identity, capabilities, IOCs, ATT&CK mapping, and detection guidance for both technical and decision-making audiences. | T1587 |
## Malware Analysis
38 skills.
| Skill | What it does | ATT&CK |
|---|---|---|
| [`analyzing-api-call-traces`](skills/analyzing-api-call-traces/SKILL.md) | Analyzes API call traces from a sandbox or API monitor (JSON) to group calls by category, reconstruct high-level behaviors (process injection, file drops, network, crypto), and flag suspicious call sequences. | T1106, T1055, T1027 |
| [`analyzing-authenticode-signatures`](skills/analyzing-authenticode-signatures/SKILL.md) | Analyzes Windows Authenticode signatures on PE files: checking for a signature, reading the signer certificate chain, detecting revoked/expired/stolen certs, and recognizing signature-stripping and catalog-signing abuse. | T1553.002, T1036.001 |
| [`analyzing-banking-trojan-webinjects`](skills/analyzing-banking-trojan-webinjects/SKILL.md) | Analyzes banking trojan webinject configurations to extract targeted institutions, injected JavaScript/HTML, and data-theft hooks, mapping the fraud workflow. | T1185, T1557, T1056.001 |
| [`analyzing-cryptominer-malware`](skills/analyzing-cryptominer-malware/SKILL.md) | Analyzes cryptojacking/coinminer malware by extracting mining pool endpoints (stratum), wallet addresses, algorithm and miner identifiers (XMRig and forks), and persistence or resource-control settings from static strings and config. | T1496, T1543, T1027 |
| [`analyzing-elf-binaries-on-linux`](skills/analyzing-elf-binaries-on-linux/SKILL.md) | Statically analyzes Linux ELF malware: ELF header and sections, dynamic symbols and imports, segment permissions, embedded strings, and packing indicators to infer capability without execution. | T1027, T1059.004, T1129 |
| [`analyzing-excel-4-macro-malware`](skills/analyzing-excel-4-macro-malware/SKILL.md) | Analyzes legacy Excel 4.0 (XLM) macro malware by parsing extracted macro-sheet formulas for auto-executing names, obfuscation (FORMULA.FILL, CHAR concatenation), and download or execution primitives (EXEC, CALL, REGISTER). | T1059.005, T1204.002, T1027 |
| [`analyzing-infostealer-credential-theft`](skills/analyzing-infostealer-credential-theft/SKILL.md) | Analyzes infostealer samples by mapping the browser, credential store, wallet, and application paths they target, and the exfiltration channel used, from static strings and behavior. | T1555.003, T1539, T1005 |
| [`analyzing-loaders-and-droppers`](skills/analyzing-loaders-and-droppers/SKILL.md) | Analyzes loader and dropper samples by identifying staging behavior — embedded or downloaded next-stage payloads, decode/decrypt routines, and the execution mechanism used to run the next stage. | T1105, T1140, T1574 |
| [`analyzing-mach-o-binaries-on-macos`](skills/analyzing-mach-o-binaries-on-macos/SKILL.md) | Statically analyzes macOS Mach-O malware: parsing the header and load commands, handling fat/universal binaries, reading linked dylibs and entitlements, and checking code signatures to infer capability and trust. | T1027, T1059.002 |
| [`analyzing-malicious-iso-and-container-files`](skills/analyzing-malicious-iso-and-container-files/SKILL.md) | Analyzes malicious ISO, IMG, VHD, and similar container files used to smuggle payloads past email and mark-of-the-web defenses by enumerating contents and flagging hidden executables, LNKs, and scripts. | T1553.005, T1204.002, T1027 |
| [`analyzing-malicious-lnk-files`](skills/analyzing-malicious-lnk-files/SKILL.md) | Analyzes weaponized Windows shortcut (.lnk) files: parsing the shell link structure for the target command, arguments, icon, and working directory, and recovering hidden PowerShell/cmd payloads and embedded content used in phishing. | T1204.002, T1059.001, T1059.003 |
| [`analyzing-malicious-office-macros`](skills/analyzing-malicious-office-macros/SKILL.md) | Analyzes malicious Office documents by extracting and reviewing VBA macros and OLE/OOXML structure: auto-exec triggers, obfuscation, dropped payloads, and shell/PowerShell invocation. | T1566.001, T1059.005, T1204.002 |
| [`analyzing-malicious-onenote-and-html-smuggling`](skills/analyzing-malicious-onenote-and-html-smuggling/SKILL.md) | Analyzes two modern delivery techniques: malicious OneNote (.one) attachments with embedded payloads behind fake buttons, and HTML smuggling pages that reconstruct payloads in the browser from embedded blobs. | T1027.006, T1204.002, T1566.001 |
| [`analyzing-malicious-pdfs`](skills/analyzing-malicious-pdfs/SKILL.md) | Analyzes malicious PDF documents: parsing the object structure for JavaScript, OpenAction/launch triggers, embedded files, and URIs, then extracting and decoding suspicious streams without opening the document in a reader. | T1204.002, T1059.007, T1027 |
| [`analyzing-malicious-vbscript-and-wsf`](skills/analyzing-malicious-vbscript-and-wsf/SKILL.md) | Analyzes malicious VBScript, WSF, and HTA scripts: parsing WSF/HTA containers, decoding VBScript.Encode (#@~^) and string obfuscation, and identifying WScript.Shell/ActiveX abuse to recover dropper behavior. | T1059.005, T1059.003, T1027 |
| [`analyzing-malware-in-memory-with-volatility3`](skills/analyzing-malware-in-memory-with-volatility3/SKILL.md) | Analyzes a memory image with Volatility 3 to find malware: rogue processes, injected code, suspicious network connections, loaded modules, and persistence, then extracts artifacts for further analysis. | T1055, T1057, T1003, T1056 |
| [`analyzing-pe-imports-and-exports`](skills/analyzing-pe-imports-and-exports/SKILL.md) | Analyzes a PE file's import and export tables to infer capability: mapping imported APIs to behaviors (networking, crypto, injection, persistence), spotting dynamic-resolution stubs, and reading exports of malicious DLLs. | T1106, T1027 |
| [`analyzing-ransomware-encryption-behavior`](skills/analyzing-ransomware-encryption-behavior/SKILL.md) | Analyzes how a ransomware sample encrypts files: identifying the crypto scheme (symmetric/asymmetric/hybrid), key handling, file targeting and extension/marker changes, shadow-copy deletion, and ransom-note artifacts. | T1486, T1490, T1083, T1027 |
| [`analyzing-rat-command-and-control`](skills/analyzing-rat-command-and-control/SKILL.md) | Analyzes remote access trojan command-and-control by mapping the RAT command set, identifying the C2 transport and beacon parameters, and cataloging operator capabilities from static strings and config. | T1219, T1071, T1105 |
| [`analyzing-rich-header-and-compiler-artifacts`](skills/analyzing-rich-header-and-compiler-artifacts/SKILL.md) | Analyzes the PE Rich header and related compiler artifacts to fingerprint the build toolchain, cluster related samples, and detect inconsistencies that suggest tampering. | T1027, T1587.001, T1036 |
| [`analyzing-webshells`](skills/analyzing-webshells/SKILL.md) | Analyzes suspected webshells (PHP, ASPX/ASP, JSP) by detecting dynamic-execution sinks, input-to-sink data flow, and common encoder/obfuscation chains, then decoding obvious layers to reveal the command interface. | T1505.003, T1059, T1027 |
| [`analyzing-wiper-malware`](skills/analyzing-wiper-malware/SKILL.md) | Analyzes destructive wiper malware by identifying raw-disk and MBR/VBR overwrite primitives, boot-record tampering, mass-deletion and backup-destruction behavior, and distinguishing wipers from ransomware. | T1485, T1561.002, T1561.001 |
| [`capturing-and-analyzing-malware-network-traffic`](skills/capturing-and-analyzing-malware-network-traffic/SKILL.md) | Captures and analyzes malware network traffic from a detonation: extracting C2 endpoints, HTTP/DNS patterns, TLS metadata (JA3/SNI), and beaconing from a PCAP to build network IOCs and detection. | T1071.001, T1071.004, T1571, T1095 |
| [`deobfuscating-malicious-javascript`](skills/deobfuscating-malicious-javascript/SKILL.md) | Deobfuscates malicious JavaScript from droppers, web pages, and HTA/scriptlets: unrolling string-array and char-code encodings, resolving eval/Function chains, and statically recovering payloads and URLs without executing untrusted code. | T1059.007, T1027, T1140 |
| [`deobfuscating-malicious-powershell`](skills/deobfuscating-malicious-powershell/SKILL.md) | Deobfuscates malicious PowerShell by decoding -EncodedCommand, reversing string and format obfuscation, resolving base64/gzip/IEX layers, and recovering the final payload and IOCs. | T1059.001, T1027, T1140 |
| [`detecting-process-injection-in-memory`](skills/detecting-process-injection-in-memory/SKILL.md) | Detects process injection in a memory image by identifying private executable regions with no file backing, RWX protections, and modified entry points using Volatility 3 malfind-style analysis. | T1055, T1055.001, T1055.012 |
| [`detecting-sandbox-evasion-behavior`](skills/detecting-sandbox-evasion-behavior/SKILL.md) | Detects sandbox and analysis evasion techniques in a sample by scanning static strings, imports, and API traces for VM artifact checks, timing/sleep stalls, anti-debug calls, and environment fingerprinting. | T1497, T1497.001, T1622 |
| [`dissecting-boot-and-kernel-rootkits`](skills/dissecting-boot-and-kernel-rootkits/SKILL.md) | Analyzes bootkit and rootkit samples by identifying boot-process tampering (MBR/VBR/ UEFI), kernel-mode components, and stealth hooking techniques from static indicators. | T1542.003, T1014, T1547.006 |
| [`extracting-and-classifying-strings`](skills/extracting-and-classifying-strings/SKILL.md) | Extracts ASCII and Unicode strings from a binary and classifies them into investigative categories: URLs, IPs, file paths, registry keys, mutexes, commands, and API names, prioritizing the analyst's attention. | T1027 |
| [`extracting-cobalt-strike-beacon-config`](skills/extracting-cobalt-strike-beacon-config/SKILL.md) | Extracts and interprets a Cobalt Strike Beacon configuration: decoding the encoded config block, recovering C2 servers, malleable profile fields, sleep/jitter, watermark, and pipe names to build IOCs and attribution leads. | T1071.001, T1573, T1027, T1090 |
| [`extracting-config-from-a-running-sample`](skills/extracting-config-from-a-running-sample/SKILL.md) | Extracts an embedded malware configuration (C2 hosts, ports, campaign IDs, keys) from a process memory dump by locating decrypted config structures and decoding common obfuscation layers. | T1027, T1140, T1071 |
| [`generating-capability-reports-with-capa`](skills/generating-capability-reports-with-capa/SKILL.md) | Uses capa to identify malware capabilities from a binary: running rule-based detection over disassembly, mapping matched capabilities to MITRE ATT&CK and MBC, and interpreting results to focus deeper analysis. | T1059, T1106, T1027 |
| [`measuring-section-entropy-to-detect-packing`](skills/measuring-section-entropy-to-detect-packing/SKILL.md) | Detects packing and encryption by measuring Shannon entropy across a binary's sections and regions: flagging high-entropy executable sections, entropy spikes, and size/raw-vs-virtual anomalies that indicate compression or encryption. | T1027.002, T1027 |
| [`monitoring-process-and-file-behavior`](skills/monitoring-process-and-file-behavior/SKILL.md) | Summarizes runtime behavior of a sample from Procmon-style operation logs — process and thread creation, file and registry writes, and network-related operations — to build a behavioral profile. | T1106, T1112, T1059 |
| [`performing-dynamic-analysis-in-a-sandbox`](skills/performing-dynamic-analysis-in-a-sandbox/SKILL.md) | Runs a sample in an instrumented sandbox to observe behavior: process tree, file and registry changes, network activity, and persistence, then summarizes the behavioral report into capabilities and IOCs. | T1055, T1071, T1547, T1497 |
| [`performing-static-pe-analysis`](skills/performing-static-pe-analysis/SKILL.md) | Extracts structure and indicators from a Windows PE file without executing it: headers, sections, imports/exports, resources, entropy, and embedded strings to infer capability and packing. | T1027, T1129, T1140 |
| [`recovering-injected-code-and-shellcode`](skills/recovering-injected-code-and-shellcode/SKILL.md) | Carves injected PE images and position-independent shellcode from a memory dump or carved region, identifies the payload type, and prepares it for disassembly or emulation. | T1055, T1620, T1027 |
| [`scanning-samples-with-yara`](skills/scanning-samples-with-yara/SKILL.md) | Uses YARA to classify and triage samples at scale: applying rule sets, reading matches and string offsets, tuning for false positives, and organizing rules for malware family identification. | T1027, T1587.001 |
## Reverse Engineering
35 skills.
| Skill | What it does | ATT&CK |
|---|---|---|
| [`analyzing-android-dex-malware`](skills/analyzing-android-dex-malware/SKILL.md) | Reverses Android malware: unpacking APKs, decompiling DEX bytecode to readable Java, auditing the manifest for abused permissions and components, and locating dynamically loaded or native payloads. | T1059, T1406, T1577 |
| [`analyzing-compiled-python-malware`](skills/analyzing-compiled-python-malware/SKILL.md) | Analyzes Python-based malware packaged as PyInstaller/py2exe executables by detecting the packer, locating the embedded archive, and identifying bundled .pyc modules for extraction and decompilation. | T1027, T1059.006, T1587.001 |
| [`analyzing-dotnet-malware-internals`](skills/analyzing-dotnet-malware-internals/SKILL.md) | Reverses .NET/managed malware: decompiling MSIL back to C#, defeating common .NET protectors and string encryptors, and tracing reflection-based loaders to recover the real payload. | T1027, T1620, T1055 |
| [`analyzing-golang-malware-internals`](skills/analyzing-golang-malware-internals/SKILL.md) | Analyzes Go-compiled malware by recovering function names from the pclntab, detecting the Go build/version string, and listing embedded package paths to overcome stripped symbols. | T1027, T1587.001, T1059 |
| [`analyzing-java-jar-malware`](skills/analyzing-java-jar-malware/SKILL.md) | Analyzes Java/JAR malware (such as Adwind/jRAT-class cross-platform RATs) by inventorying the archive, reading the manifest entry point, detecting obfuscators and string encryption, and flagging suspicious runtime, reflection, and networking class usage. | T1059, T1027, T1620 |
| [`analyzing-position-independent-code`](skills/analyzing-position-independent-code/SKILL.md) | Analyzes position-independent code and shellcode by identifying GetPC/PEB-walk techniques, API-hash resolution loops, and relocation-free addressing so the payload can be disassembled at the right base. | T1027, T1027.007, T1620 |
| [`analyzing-rust-malware-internals`](skills/analyzing-rust-malware-internals/SKILL.md) | Analyzes Rust-compiled malware by detecting the Rust toolchain signature, demangling Rust v0/legacy symbol names, and identifying crate dependencies from embedded paths. | T1027, T1587.001, T1059 |
| [`analyzing-windows-driver-malware`](skills/analyzing-windows-driver-malware/SKILL.md) | Analyzes malicious and vulnerable Windows kernel drivers (.sys) by parsing the PE for the native subsystem, identifying DriverEntry/IRP dispatch and IOCTL handlers, and flagging BYOVD and kernel-callback abuse. | T1068, T1014, T1547.006 |
| [`automating-analysis-with-r2pipe`](skills/automating-analysis-with-r2pipe/SKILL.md) | Automates radare2/rizin analysis through r2pipe to script function enumeration, string and import extraction, and cross-reference queries across samples programmatically. | T1027, T1059, T1587.001 |
| [`building-config-extractors`](skills/building-config-extractors/SKILL.md) | Builds reusable malware configuration extractors by applying a declarative JSON spec (offsets, decode layers, field definitions) to a sample to produce structured, defanged config. | T1140, T1027, T1071 |
| [`bypassing-anti-vm-and-sandbox-checks`](skills/bypassing-anti-vm-and-sandbox-checks/SKILL.md) | Bypasses anti-VM and sandbox checks during analysis by locating the specific detection routines (artifact strings, timing, CPUID/hypervisor bit) and planning patches or environment hardening so the sample detonates. | T1497, T1497.001, T1497.003 |
| [`debugging-malware-with-x64dbg`](skills/debugging-malware-with-x64dbg/SKILL.md) | Uses x64dbg to dynamically debug Windows malware: setting strategic breakpoints on APIs, stepping through unpacking and decryption, dumping memory at the right moment, and manipulating execution to reach hidden code. | T1027, T1140, T1497, T1055 |
| [`decrypting-embedded-configuration`](skills/decrypting-embedded-configuration/SKILL.md) | Decrypts statically embedded malware configuration blobs by trying common schemes (single-byte and multi-byte XOR, RC4, base64 layers) and scoring decoded output for config-like content. | T1140, T1027, T1071 |
| [`defeating-control-flow-flattening`](skills/defeating-control-flow-flattening/SKILL.md) | Defeats control-flow-flattening obfuscation by identifying the dispatcher/state- variable structure and reconstructing the original control flow so the logic becomes readable. | T1027, T1027.013, T1140 |
| [`defeating-string-and-api-obfuscation`](skills/defeating-string-and-api-obfuscation/SKILL.md) | Recovers obfuscated strings and resolves dynamically loaded APIs in malware: reversing XOR/stack-string/encoding schemes and mapping API-hash lookups back to function names. | T1027, T1140, T1106 |
| [`diffing-malware-samples-to-find-changes`](skills/diffing-malware-samples-to-find-changes/SKILL.md) | Compares two related malware samples to surface what changed between variants using section-level hashing, import-set differences, and fuzzy similarity scoring. | T1587.001, T1027, T1588.001 |
| [`dumping-and-rebuilding-a-pe-from-memory`](skills/dumping-and-rebuilding-a-pe-from-memory/SKILL.md) | Rebuilds a usable PE file from a memory dump by fixing the section alignment (virtual-to-raw), correcting the entry point, and preparing for import table reconstruction. | T1027.002, T1620, T1055.012 |
| [`emulating-shellcode-with-unicorn`](skills/emulating-shellcode-with-unicorn/SKILL.md) | Emulates position-independent shellcode in a controlled CPU emulator (Unicorn) to trace executed instructions, memory writes, and decoded second stages without running it on a real host. | T1027, T1620, T1055 |
| [`extracting-encryption-keys-from-binaries`](skills/extracting-encryption-keys-from-binaries/SKILL.md) | Locates candidate encryption keys in a binary by finding high-entropy fixed-size regions (16/24/32 bytes) near cryptographic constants or crypto API references, and surfacing printable key/passphrase strings. | T1027, T1573, T1486 |
| [`identifying-anti-debugging-techniques`](skills/identifying-anti-debugging-techniques/SKILL.md) | Identifies and bypasses anti-debugging and anti-analysis checks in malware: PEB flags, debugger-detection APIs, timing checks, and exception tricks, then neutralizes them to continue analysis. | T1622, T1497.001, T1027 |
| [`identifying-cryptographic-routines-in-binaries`](skills/identifying-cryptographic-routines-in-binaries/SKILL.md) | Identifies cryptographic algorithms embedded in a binary by scanning for well-known constants and tables (AES S-box, SHA-256/MD5 init constants, ChaCha sigma, CRC32 table, base64 alphabet). | T1027, T1486, T1573 |
| [`manually-unpacking-a-packed-binary`](skills/manually-unpacking-a-packed-binary/SKILL.md) | Manually unpacks a runtime-packed Windows binary by finding the original entry point (OEP) through tail-jump and entropy analysis, then guiding a memory dump at OEP. | T1027.002, T1140, T1620 |
| [`resolving-dynamic-api-hashing`](skills/resolving-dynamic-api-hashing/SKILL.md) | Resolves dynamically hashed Windows API names by brute-forcing observed hash constants against a wordlist of API/DLL names using common malware hashing algorithms (ROR13, djb2, FNV, CRC32). | T1027.007, T1106, T1140 |
| [`reverse-engineering-arm-binaries`](skills/reverse-engineering-arm-binaries/SKILL.md) | Reverse engineers ARM/AArch64 malware by identifying the architecture and instruction set state (ARM/Thumb), parsing ELF/Mach-O ARM headers, and orienting analysis around the ARM calling convention. | T1027, T1059, T1587.001 |
| [`reverse-engineering-binaries-with-binary-ninja`](skills/reverse-engineering-binaries-with-binary-ninja/SKILL.md) | Reverse engineers binaries with Binary Ninja using its analysis stack and Python API to enumerate functions, navigate IL levels (LLIL/MLIL/HLIL), and automate annotation and extraction. | T1027, T1059, T1587.001 |
| [`reverse-engineering-binaries-with-ghidra`](skills/reverse-engineering-binaries-with-ghidra/SKILL.md) | Uses Ghidra to disassemble and decompile a binary, navigate to key routines via imports and strings, annotate decompiled code, and run headless scripts to automate extraction of C2, crypto, and config. | T1027, T1140, T1480 |
| [`reverse-engineering-binaries-with-radare2-rizin`](skills/reverse-engineering-binaries-with-radare2-rizin/SKILL.md) | Reverse engineers binaries using radare2/rizin interactively, covering analysis initialization, navigation, disassembly and graph views, patching, and the visual mode workflow. | T1027, T1059, T1587.001 |
| [`reverse-engineering-custom-c2-protocols`](skills/reverse-engineering-custom-c2-protocols/SKILL.md) | Reverses proprietary command-and-control protocols: locating send/recv routines, recovering the message framing and encryption/encoding, and reconstructing the command set to build a decoder or emulator. | T1071, T1095, T1573 |
| [`reverse-engineering-nim-and-other-exotic-binaries`](skills/reverse-engineering-nim-and-other-exotic-binaries/SKILL.md) | Reverse engineers malware written in less-common compiled languages (Nim, Crystal, V, Zig, D) by detecting language-specific runtime signatures, name-mangling, and runtime strings to orient analysis. | T1027, T1587.001, T1059 |
| [`reverse-engineering-shellcode`](skills/reverse-engineering-shellcode/SKILL.md) | Analyzes position-independent shellcode: disassembling raw bytes at the right architecture, recognizing PEB-walk API resolution and egg hunters, and emulating execution to recover behavior and payloads. | T1055, T1106, T1027 |
| [`scripting-ghidra-headless-analysis`](skills/scripting-ghidra-headless-analysis/SKILL.md) | Automates batch reverse engineering by generating Ghidra analyzeHeadless commands and post-analysis script skeletons to import, auto-analyze, and export results for many samples without the GUI. | T1027, T1059, T1587.001 |
| [`scripting-ida-with-idapython`](skills/scripting-ida-with-idapython/SKILL.md) | Automates IDA Pro analysis with IDAPython by generating scripts that enumerate functions, decode strings, rename by signature, and export analysis results, and by documenting the headless idat batch invocation. | T1027, T1059, T1620 |
| [`solving-malware-with-symbolic-execution`](skills/solving-malware-with-symbolic-execution/SKILL.md) | Uses symbolic execution (angr) to solve constraint-based malware problems — finding inputs that reach a target branch, recovering keys/passwords from a check routine, and brute-forcing opaque predicates — by generating a ready-to-edit angr harness. | T1027, T1480, T1140 |
| [`unpacking-upx-and-common-packers`](skills/unpacking-upx-and-common-packers/SKILL.md) | Identifies and unpacks packed binaries: detecting packers from section names and entropy, automatically unpacking UPX, and manually unpacking custom packers by finding the OEP and dumping. | T1027.002, T1140, T1480 |
| [`writing-yara-rules-from-reversed-code`](skills/writing-yara-rules-from-reversed-code/SKILL.md) | Turns reverse-engineering findings into durable YARA detections: selecting stable code constructs and constants over volatile strings, extracting opcode/byte patterns with wildcards, and validating rules for low false positives. | T1027.001, T1587.001 |
## Threat Hunting
33 skills.
| Skill | What it does | ATT&CK |
|---|---|---|
| [`building-a-threat-hunt-hypothesis`](skills/building-a-threat-hunt-hypothesis/SKILL.md) | Frames a structured, testable threat-hunting hypothesis: grounding it in adversary behavior and available telemetry, defining the data sources and detection logic, and setting success criteria and outcomes. | T1059, T1071 |
| [`building-zeek-analytics-for-hunting`](skills/building-zeek-analytics-for-hunting/SKILL.md) | Builds Zeek-based network hunting analytics by writing scripts and analyzing Zeek logs (conn, dns, http, ssl, files) to surface long connections, rare JA3s, suspicious downloads, and beaconing. | T1071, T1095, T1571 |
| [`establishing-telemetry-baselines`](skills/establishing-telemetry-baselines/SKILL.md) | Establishes behavioral baselines from historical telemetry (process, network, or logon events) so hunts can flag rare and first-seen activity instead of relying on static signatures. | T1059, T1071, T1204 |
| [`hunting-active-directory-attacks`](skills/hunting-active-directory-attacks/SKILL.md) | Hunts for Active Directory credential and replication attacks — DCSync, Golden/Silver Ticket, and DCShadow — by analyzing directory-service-access (4662), Kerberos TGS (4769), and logon (4624) events for replication abuse and forged-ticket anomalies. | T1003.006, T1558.001, T1207 |
| [`hunting-anomalous-authentication-patterns`](skills/hunting-anomalous-authentication-patterns/SKILL.md) | Hunts for anomalous authentication such as password spraying, brute force, and impossible-travel logons by aggregating Windows logon success/failure events per account and source over time. | T1110.003, T1110, T1078 |
| [`hunting-c2-beaconing-with-frequency-analysis`](skills/hunting-c2-beaconing-with-frequency-analysis/SKILL.md) | Detects command-and-control beacons in network logs by analyzing connection timing: measuring inter-arrival intervals, accounting for jitter, and scoring regularity per source/destination pair to surface periodic callbacks. | T1071.001, T1071, T1573 |
| [`hunting-cobalt-strike-traffic`](skills/hunting-cobalt-strike-traffic/SKILL.md) | Hunts for Cobalt Strike beacon traffic by detecting default stager URI checksum8 values, default Malleable C2 profile artifacts, and default ports/named pipes in HTTP and process telemetry. | T1071.001, T1573, T1090 |
| [`hunting-credential-dumping-activity`](skills/hunting-credential-dumping-activity/SKILL.md) | Hunts for credential dumping by detecting LSASS process access with suspicious access masks, known dumping tool signatures, and comsvcs.exe MiniDump abuse in Sysmon Event ID 10 and process-creation telemetry. | T1003.001, T1003, T1003.002 |
| [`hunting-data-staging-and-archiving`](skills/hunting-data-staging-and-archiving/SKILL.md) | Hunts for pre-exfiltration data staging and archiving by detecting archive-tool execution (rar, 7z, zip, tar), large compressed files written to staging directories, and collection into a single location before transfer. | T1074.001, T1560.001, T1560 |
| [`hunting-dns-tunneling-and-exfiltration`](skills/hunting-dns-tunneling-and-exfiltration/SKILL.md) | Detects DNS-based tunneling and data exfiltration in DNS logs: scoring high query volume to single domains, long/high-entropy subdomains, unusual record types, and encoded payload labels. | T1071.004, T1048.003, T1572 |
| [`hunting-domain-fronting-and-cdn-abuse`](skills/hunting-domain-fronting-and-cdn-abuse/SKILL.md) | Hunts for domain fronting and CDN abuse by detecting TLS SNI versus HTTP Host header mismatches and high-reputation CDN domains carrying anomalous traffic in network telemetry. | T1090.004, T1071.001, T1573 |
| [`hunting-fileless-and-in-memory-threats`](skills/hunting-fileless-and-in-memory-threats/SKILL.md) | Hunts for fileless and in-memory threats by correlating PowerShell script-block logs, encoded-command usage, in-memory loading patterns, and registry-resident payloads from Windows telemetry. | T1059.001, T1620, T1112 |
| [`hunting-from-a-threat-intel-report`](skills/hunting-from-a-threat-intel-report/SKILL.md) | Operationalizes a threat intelligence report into hunts: extracting IOCs and TTPs, mapping behaviors to ATT&CK and available telemetry, prioritizing durable behavioral indicators over volatile atomic ones, and producing concrete hunt queries. | T1059, T1071, T1547 |
| [`hunting-kerberoasting-and-ticket-attacks`](skills/hunting-kerberoasting-and-ticket-attacks/SKILL.md) | Hunts for Kerberoasting and related ticket attacks by analyzing Kerberos service ticket request events (4769) for weak encryption types, abnormal request volume per account, and non-machine accounts requesting many SPNs. | T1558.003, T1558, T1550.003 |
| [`hunting-lateral-movement-over-smb-and-wmi`](skills/hunting-lateral-movement-over-smb-and-wmi/SKILL.md) | Hunts for lateral movement via remote service creation, admin share writes, and remote WMI process creation by correlating Windows logon, service install, and process-creation events. | T1021.002, T1021.003, T1570 |
| [`hunting-lolbin-abuse-on-windows`](skills/hunting-lolbin-abuse-on-windows/SKILL.md) | Hunts for living-off-the-land binary (LOLBin) abuse in Windows process-creation telemetry by flagging trusted system binaries used with download, execution-proxy, or encoded-command arguments. | T1218, T1105, T1059 |
| [`hunting-persistence-mechanisms-on-windows`](skills/hunting-persistence-mechanisms-on-windows/SKILL.md) | Hunts for Windows persistence across autostart locations: Run keys, services, scheduled tasks, WMI event subscriptions, and startup folders, baselining them to surface anomalous or recently added entries. | T1547.001, T1543.003, T1053.005, T1546.003 |
| [`hunting-persistence-on-linux`](skills/hunting-persistence-on-linux/SKILL.md) | Hunts for Linux persistence by inspecting cron, systemd units, shell rc files, SSH authorized_keys, and common autostart locations for attacker-added entries. | T1053.003, T1543.002, T1098.004 |
| [`hunting-process-injection-with-sysmon`](skills/hunting-process-injection-with-sysmon/SKILL.md) | Hunts for process injection using Sysmon telemetry: correlating CreateRemoteThread (EID 8), suspicious cross-process access (EID 10), and RWX image-less memory to surface hollowing, shellcode injection, and APC abuse. | T1055, T1055.001, T1055.012 |
| [`hunting-ransomware-precursor-behavior`](skills/hunting-ransomware-precursor-behavior/SKILL.md) | Hunts for ransomware precursor behavior that precedes encryption — shadow-copy and backup deletion, recovery disabling, security-tool tampering, and mass file modification — so the attack can be stopped before payload detonation. | T1490, T1489, T1486 |
| [`hunting-scheduled-task-abuse`](skills/hunting-scheduled-task-abuse/SKILL.md) | Hunts for malicious scheduled task persistence by analyzing task registration events (4698) and schtasks process creation for suspicious actions, triggers, and hidden tasks. | T1053.005, T1053, T1059 |
| [`hunting-suspicious-powershell-execution`](skills/hunting-suspicious-powershell-execution/SKILL.md) | Hunts malicious PowerShell using script-block (EID 4104) and module logging: scoring encoded commands, download cradles, AMSI/logging bypass, and in-memory execution, then decoding payloads for triage. | T1059.001, T1027, T1562.001 |
| [`hunting-unusual-outbound-connections`](skills/hunting-unusual-outbound-connections/SKILL.md) | Hunts for unusual outbound network connections by flagging direct-to-IP traffic, uncommon ports, rare destinations, high data-egress ratios, and connections from unexpected processes in network telemetry. | T1071, T1048, T1571 |
| [`hunting-wmi-event-subscription-persistence`](skills/hunting-wmi-event-subscription-persistence/SKILL.md) | Hunts for malicious WMI permanent event subscription persistence by correlating __EventFilter, CommandLineEventConsumer/ActiveScriptEventConsumer, and FilterToConsumerBinding records from Sysmon Event IDs 19/20/21 or WMI repository exports. | T1546.003, T1047, T1059 |
| [`mapping-hunts-to-mitre-attack`](skills/mapping-hunts-to-mitre-attack/SKILL.md) | Maps hunts and detections to MITRE ATT&CK for coverage analysis: tagging hypotheses with techniques, building a coverage matrix from completed hunts, and identifying high-risk gaps to prioritize next. | T1059, T1047, T1071 |
| [`operationalizing-a-hunt-into-a-detection`](skills/operationalizing-a-hunt-into-a-detection/SKILL.md) | Converts a successful threat hunt into a durable detection by extracting the discriminating logic, defining data sources and thresholds, and emitting a Sigma rule plus a test and tuning plan. | T1059, T1071, T1053 |
| [`pivoting-on-iocs-across-data-sources`](skills/pivoting-on-iocs-across-data-sources/SKILL.md) | Pivots on indicators of compromise across multiple data sources by correlating a seed set of IOCs against logs to find co-occurring indicators, hosts, and timeframes that expand the investigation. | T1071, T1041, T1105 |
| [`profiling-a-threat-actor-from-ttps`](skills/profiling-a-threat-actor-from-ttps/SKILL.md) | Profiles a threat actor by aggregating observed ATT&CK techniques into a TTP profile and comparing it against known-group technique sets to estimate similarity and candidate attribution. | T1059, T1071, T1583 |
| [`tracking-adversary-infrastructure`](skills/tracking-adversary-infrastructure/SKILL.md) | Tracks adversary C2 and staging infrastructure by clustering indicators on shared attributes (TLS/JARM, certificate fields, favicon hashes, ASN, registration patterns) to find related hosts. | T1583, T1583.001, T1071 |
| [`tuning-detections-to-reduce-false-positives`](skills/tuning-detections-to-reduce-false-positives/SKILL.md) | Tunes noisy detections by analyzing historical alert data to quantify false-positive sources, recommend allow-list filters and threshold adjustments, and measure precision impact. | T1059, T1071, T1053 |
| [`validating-detections-with-atomic-tests`](skills/validating-detections-with-atomic-tests/SKILL.md) | Validates detection coverage by mapping Atomic Red Team tests to ATT&CK techniques, checking which of your detections cover each tested technique, and reporting coverage gaps. | T1059, T1003, T1053 |
| [`writing-sigma-detection-rules`](skills/writing-sigma-detection-rules/SKILL.md) | Converts hunt findings into portable Sigma detection rules: choosing the right logsource, expressing robust selection/filter logic, setting level and ATT&CK tags, and validating before converting to a SIEM query. | T1059.001, T1547.001 |
| [`writing-suricata-and-snort-rules`](skills/writing-suricata-and-snort-rules/SKILL.md) | Writes and validates Suricata/Snort IDS rules for network-based malware detection, covering rule anatomy, content/pcre matching, flow and threshold options, and common mistakes that cause false positives or no matches. | T1071.001, T1095, T1571 |

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cff-version: 1.2.0
message: "If you use AnalystAIPack, please cite it as below."
title: AnalystAIPack
abstract: >-
An open agent-skills library for malware analysis, reverse engineering, and
threat hunting. 118 curated skills, each shipping a tested, runnable analysis
script, mapped to MITRE ATT&CK, D3FEND, and CAR.
type: software
authors:
- family-names: Sadique
given-names: Mohd
alias: meltedinhex
website: "https://meltedinhex.com/"
repository-code: "https://github.com/meltedinhex/analyst-ai-pack"
url: "https://github.com/meltedinhex/analyst-ai-pack"
license: Apache-2.0
version: "1.0.0"
date-released: "2026-07-01"
keywords:
- malware-analysis
- reverse-engineering
- threat-hunting
- agent-skills
- mitre-attack
- dfir
- detection-engineering

43
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# Code of Conduct
## Our pledge
We pledge to make participation in AnalystAIPack a harassment-free experience for everyone,
regardless of age, body size, disability, ethnicity, gender identity and expression, level
of experience, nationality, personal appearance, race, religion, or sexual identity and
orientation.
## Our standards
Examples of behavior that contributes to a positive environment:
- Using welcoming and inclusive language
- Respecting differing viewpoints and experiences
- Gracefully accepting constructive feedback
- Focusing on what is best for the community
- Showing empathy toward other community members
Examples of unacceptable behavior:
- Sexualized language or imagery, or unwelcome sexual attention
- Trolling, insulting or derogatory comments, and personal or political attacks
- Public or private harassment
- Publishing others' private information without explicit permission
- Other conduct which could reasonably be considered inappropriate in a professional setting
## Scope
This Code of Conduct applies within all project spaces and in public spaces when an
individual is representing the project.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the
maintainers. All complaints will be reviewed and investigated promptly and fairly.
Maintainers are obligated to respect the privacy and security of the reporter of any
incident.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org),
version 2.1.

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# Contributing to AnalystAIPack
Thanks for helping build AnalystAIPack. This guide covers how skills are structured, the
authoring checklist, and — most importantly — the originality rules that keep this library's
content genuinely its own.
## Originality rules (non-negotiable)
AnalystAIPack reuses an **open file format** (agentskills.io) on purpose, but its **content is
original**. To keep that true:
1. **Author from primary sources only.** Do not open, read, or paraphrase any skill file
from another skills repository while writing an AnalystAIPack skill. Work from tool docs,
MITRE pages, specs, and research. Other skills repos may be consulted **only** to
understand the file format, never the content.
2. **Distinct, content-accurate names.** Where an equivalent skill exists elsewhere, choose
a different, accurate name.
3. **Cite real sources.** Every skill's `references/api-reference.md` lists the specific
primary sources it was synthesized from (specific pages/specs/reports, not homepages).
4. **Original scripts.** All `analyst.py` code is written and tested fresh, and linted with
`ruff`. No lifted or reworded code.
5. **Run the similarity gate.** Before marking a skill done, sanity-check that its prose
does not closely track any single external source. High overlap → rewrite.
## Skill anatomy
```
skills/<skill-name>/
├── SKILL.md
├── scripts/analyst.py
├── references/api-reference.md
└── LICENSE
```
Start from the scaffold:
```bash
cp -r templates/skill-template skills/<skill-name>
# PowerShell: Copy-Item -Recurse templates/skill-template skills/<skill-name>
```
### SKILL.md frontmatter
| Field | Required | Notes |
|---|---|---|
| `name` | yes | kebab-case, **must equal the folder name** |
| `description` | yes | 1–3 sentences, ≥ 50 chars, ends with activation trigger |
| `domain` | yes | always `cybersecurity` |
| `subdomain` | yes | one of `lab-foundations`, `malware-analysis`, `reverse-engineering`, `threat-hunting` |
| `tags` | yes | 3–8 lowercase tags |
| `version` | yes | semver, start at `1.0.0` |
| `author` | yes | your handle |
| `license` | yes | `Apache-2.0` |
| `mitre_attack` | yes | ≥ 1 valid `Txxxx` / `Txxxx.yyy` id |
| `d3fend` | optional | `D3-XXX` ids where a countermeasure applies |
| `car` | optional | `CAR-YYYY-MM-NNN` ids for hunting/detection skills |
| `references` | optional | source list (full list also goes in `references/`) |
### SKILL.md body — required section order
1. `# Title`
2. `## When to Use` — triggers **and** an explicit *Do not use*
3. `## Prerequisites`
4. `## Safety & Handling` — **only** for sample-handling skills (omit otherwise)
5. `## Workflow` — numbered steps, real commands/code
6. `## Validation`
7. `## Pitfalls`
8. `## References`
Keep steps concrete: name the actual tools, flags, and artifacts. Avoid generic advice.
## Authoring checklist
- [ ] Folder + `SKILL.md` + `scripts/analyst.py` + `references/api-reference.md` + `LICENSE`
- [ ] Frontmatter passes the validator
- [ ] `When to Use` has an explicit *Do not use*
- [ ] `Safety & Handling` present for any sample-handling skill
- [ ] Workflow steps are concrete and runnable
- [ ] `analyst.py` runs and is original; `ruff` clean
- [ ] ATT&CK / D3FEND / CAR ids verified against current versions
- [ ] Sources cited in `references/api-reference.md`; originality self-check done
- [ ] `index.json` regenerated
## Validate and build
```bash
python tools/lint-skills.py skills/<skill-name> # one skill
python tools/lint-skills.py --all # everything
python tools/build-index.py # regenerate index.json
python tools/build-index.py --check # CI: fail if index is stale
python tools/build-catalog.py # regenerate CATALOG.md
python tools/build-navigator-layer.py # regenerate the ATT&CK layer
python tools/smoke-test.py # run every analyst.py (--help + fixtures)
ruff check skills/<skill-name>/scripts # lint tooling
```
A pull request should pass `lint-skills.py --all`, the `--check` modes
(`build-index.py`, `build-catalog.py`, `build-navigator-layer.py`), `smoke-test.py`, and
`ruff`. These run automatically in CI (`.github/workflows/ci.yml`).
## Commit and PR
- One skill (or one coherent set) per PR.
- Describe the sources you synthesized from in the PR body.
- Confirm the originality rules were followed.

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# AnalystAIPack
[![CI](https://github.com/meltedinhex/analyst-ai-pack/actions/workflows/ci.yml/badge.svg)](https://github.com/meltedinhex/analyst-ai-pack/actions/workflows/ci.yml)
[![License: Apache-2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](LICENSE)
[![Skills](https://img.shields.io/badge/skills-118-green.svg)](CATALOG.md)
[![Format: agentskills.io](https://img.shields.io/badge/format-agentskills.io-8A2BE2.svg)](https://agentskills.io)
**An open agent-skills library for malware analysis, reverse engineering, and threat hunting.**
AnalystAIPack gives an AI agent the working knowledge of a malware analyst and threat hunter:
which Volatility 3 plugin to run on a memory image, how to unpack a packed sample, how to
hunt C2 beaconing in network logs, and how to turn findings into YARA and Sigma. It is a
**focused, depth-first** library — 118 curated skills across four tightly-scoped subdomains,
where **every skill ships a tested, runnable analysis script**, not just prose.
![AnalystAIPack in action — an agent chaining skills from sample to detection](assets/AnalystAIPackDemo.gif)
> The name says what it is: a **pack** of ready-to-load **AI** agent skills for the security
> **analyst** — covering the full workflow to **hunt**, **reverse**, and **analyze** malicious
> code.
> **Independent project.** Not affiliated with Anthropic, MITRE, or any vendor named here.
> Built from scratch and synthesized from many primary sources; not copied from any other
> skills repository. See [CONTRIBUTING.md](CONTRIBUTING.md) for the sourcing and originality policy.
>
> **Personal work.** A personal, independent project maintained in a personal capacity. It is
> not affiliated with, endorsed by, or representative of the maintainer's employer.
## What makes it different
- **Depth over breadth.** Four deliberate subdomains (malware analysis, reverse engineering,
threat hunting, and shared lab foundations) instead of a sprawling catalog — every skill is
curated for the analyst's real workflow.
- **Every skill is runnable.** All 118 skills ship a tested `scripts/analyst.py` (standard
library only, optional deps degrade gracefully) plus a repo-addwide smoke-test harness and CI
`--check` gates — so the tooling actually works, not just reads well.
- **Safe by construction.** Scripts perform static, read-only analysis and **never execute the
sample**; IOCs are defanged in output; sample-handling skills carry an explicit
`Safety & Handling` section.
- **A defender's framework lens.** Skills map to **MITRE ATT&CK**, **MITRE D3FEND**, and
**MITRE CAR** — chosen because they fit RE / malware analysis / threat hunting far better than
compliance checklists.
- **Consistent, opinionated shape.** Each skill follows the same body contract — *When to Use*
(with an explicit **Do not use**), *Workflow*, *Validation*, and *Pitfalls* — so an agent
always knows the boundaries.
## Scope
Three pillars plus a shared foundation:
| Subdomain | What it covers |
|---|---|
| `lab-foundations` | Safe handling, lab setup, triage, hashing, file ID, IOC formats, reporting |
| `malware-analysis` | Static, dynamic, behavioral, and memory analysis; document/script malware; families |
| `reverse-engineering` | Disassembly/decompilation, unpacking, deobfuscation, anti-analysis defeat, language-specific RE |
| `threat-hunting` | Hypothesis-driven hunts, endpoint/network/identity telemetry, detection engineering |
See [taxonomy.md](taxonomy.md) for naming rules and framework mappings. Browse every skill in
[CATALOG.md](CATALOG.md), and see ATT&CK coverage in
[mappings/](mappings/README.md).
## Who it's for
- **SOC analysts & incident responders** — triage an unknown file, pull IOCs, and pivot them
across telemetry without re-deriving the steps each time.
- **Malware analysts & reverse engineers** — a consistent procedure (and a runnable script)
for static analysis, unpacking, config extraction, and language-specific RE.
- **Threat hunters & detection engineers** — turn a finding into a Sigma / YARA / Suricata
detection and validate coverage against ATT&CK.
- **AI-agent builders** — drop a vetted, safety-bounded security skill set into Claude Code,
Copilot, Cursor, or any agentskills.io-compatible agent.
## A worked example: from sample to detection
Each skill is a step; chained together they cover the full analyst loop. A typical
investigation of a suspicious executable walks through skills like these:
| # | Stage | Skill |
|---|---|---|
| 1 | Triage the unknown file | [`triaging-an-unknown-sample`](skills/triaging-an-unknown-sample/SKILL.md) |
| 2 | Static PE inspection | [`performing-static-pe-analysis`](skills/performing-static-pe-analysis/SKILL.md) |
| 3 | Spot packing via entropy | [`measuring-section-entropy-to-detect-packing`](skills/measuring-section-entropy-to-detect-packing/SKILL.md) |
| 4 | Unpack to the OEP | [`manually-unpacking-a-packed-binary`](skills/manually-unpacking-a-packed-binary/SKILL.md) |
| 5 | Recover the C2 config | [`extracting-cobalt-strike-beacon-config`](skills/extracting-cobalt-strike-beacon-config/SKILL.md) |
| 6 | Defang and package IOCs | [`defanging-and-sharing-iocs`](skills/defanging-and-sharing-iocs/SKILL.md) |
| 7 | Hunt the IOCs in traffic | [`hunting-cobalt-strike-traffic`](skills/hunting-cobalt-strike-traffic/SKILL.md) |
| 8 | Write a durable detection | [`writing-sigma-detection-rules`](skills/writing-sigma-detection-rules/SKILL.md) |
Driven directly from the CLI, the mechanical steps look like this:
```bash
# 1. Triage, then 5. recover the beacon config (read-only, never executes the sample)
python tools/analyst-pack.py run triaging-an-unknown-sample -- triage suspicious.exe
python tools/analyst-pack.py run extracting-cobalt-strike-beacon-config -- parse beacon.bin
# 7. Hunt the recovered indicators in proxy/Zeek logs, 8. emit a Sigma rule
python tools/analyst-pack.py run hunting-cobalt-strike-traffic -- hunt http.csv
```
Every script prints structured, **defanged** JSON you can feed into a report, a SIEM, or the
next skill in the chain.
## Format
Every skill follows the [agentskills.io](https://agentskills.io) `SKILL.md` standard, so it
works in Claude Code, GitHub Copilot, Cursor, Codex CLI, Gemini CLI, and other compatible
agents.
```
skills/<skill-name>/
├── SKILL.md # frontmatter + procedure (When to Use, Workflow, ...)
├── scripts/analyst.py # runnable tooling
├── references/api-reference.md
└── LICENSE
```
Skills map to **MITRE ATT&CK**, **MITRE D3FEND**, and **MITRE CAR** (for hunts) in
frontmatter — frameworks chosen because they fit reverse engineering, malware analysis, and
threat hunting better than compliance checklists.
## Quick start
```bash
git clone https://github.com/meltedinhex/analyst-ai-pack.git
cd analyst-ai-pack
# Validate skills and (re)build the catalog
python tools/lint-skills.py --all
python tools/build-index.py
# (Re)build the browsable catalog and ATT&CK Navigator layer
python tools/build-catalog.py
python tools/build-navigator-layer.py
```
Point your agent at the `skills/` directory, or load an individual `SKILL.md`. AI agents
should read [AGENTS.md](AGENTS.md) first — it explains how to find, run, and safely chain
skills.
## Use with GitHub Copilot
Open this folder in VS Code. Copilot Chat automatically reads
[`.github/copilot-instructions.md`](.github/copilot-instructions.md), so it knows the skills
exist and how to run them safely. Then, in **Agent** mode, just ask:
- *"Hunt for LOLBin abuse in events.csv"* → uses `hunting-lolbin-abuse-on-windows`
- *"Triage this unknown file and pull IOCs"* → chains triage → IOC extraction
You can also point Copilot at a specific skill:
```
#file:skills/extracting-cobalt-strike-beacon-config/SKILL.md
Use this skill to analyze beacon.bin
```
No subscription beyond Copilot is required — the scripts are plain Python and run via Copilot's
terminal tool (or directly with `python tools/analyst-pack.py run …`).
## The `analyst-pack` CLI
A single front door to discover and run the library — unique to AnalystAIPack:
```bash
python tools/analyst-pack.py list --subdomain threat-hunting # browse skills
python tools/analyst-pack.py search kerberos # full-text search
python tools/analyst-pack.py show hunting-lolbin-abuse-on-windows
python tools/analyst-pack.py run identifying-cryptographic-routines-in-binaries -- scan a.bin
python tools/analyst-pack.py stats # library statistics
python tools/analyst-pack.py lint # lint every skill
python tools/analyst-pack.py build # rebuild index/catalog/layer
```
## Creating a skill
```bash
# Copy the scaffold
cp -r templates/skill-template skills/my-new-skill # PowerShell: Copy-Item -Recurse
# Edit SKILL.md, scripts/analyst.py, references/api-reference.md, then:
python tools/lint-skills.py skills/my-new-skill
python tools/build-index.py
```
Read [CONTRIBUTING.md](CONTRIBUTING.md) first — it covers the authoring checklist and the
originality rules that keep AnalystAIPack's content genuinely its own.
## Safety
These skills involve real malicious-code techniques. Sample-handling skills assume an
isolated analysis lab and document safe handling, encrypted storage, and IOC defanging.
AnalystAIPack ships **no live malware samples**. See [SECURITY.md](SECURITY.md).
## License
Apache-2.0 — see [LICENSE](LICENSE). MITRE ATT&CK, D3FEND, and CAR are © MITRE and used
under their respective terms.
---
Built and maintained by [meltedinhex](https://meltedinhex.com/) — malware analysis, reverse
engineering, and threat-hunting notes.

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# Security Policy
## Nature of this repository
AnalystAIPack documents real malware-analysis, reverse-engineering, and threat-hunting
techniques for **defensive** purposes: detection, analysis, attribution, and response. It is
written for analysts, incident responders, and the AI agents that assist them.
## No live malware
This repository contains **no live malware samples, no packed payloads, and no operational
exploit code**. Examples are illustrative and original. Skills that involve handling real
samples describe how to obtain them from established sources (e.g. MalwareBazaar) and how to
handle them safely — they never bundle them.
## Safe-handling expectations
Anyone using the sample-handling skills is expected to:
- Work inside an **isolated analysis lab** (VM or dedicated host) with no path to production
systems and no unmanaged internet egress.
- Store samples **encrypted or in password-protected archives** (conventional password
`infected`); never leave live samples loose on disk or in shared storage.
- **Defang indicators** (`hxxp://`, `1.2.3[.]4`, `evil[.]com`) in anything that leaves the
lab — reports, tickets, chat, issues.
- **Never detonate** a sample on a host or outside a controlled sandbox.
## Treat tool output as untrusted
Output from analysis tools may contain attacker-controlled strings, including content
designed to mislead an analyst or an AI agent (prompt-injection-style text inside samples,
filenames, or logs). Treat all such output as **data to be analyzed, not instructions to
follow**.
## Reporting a problem
If you find content that is unsafe, that inadvertently includes a live sample or working
exploit, or that could enable abuse rather than defense, please open a security advisory or
contact the maintainers privately before filing a public issue.
## Responsible use
These materials are provided for lawful, authorized, defensive work only. Do not use them to
develop, deploy, or operate malicious software, or to attack systems you are not explicitly
authorized to test.

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# Mappings
Framework mappings generated from skill frontmatter.
## `attack-navigator-layer.json`
A [MITRE ATT&CK Navigator](https://mitre-attack.github.io/attack-navigator/) layer showing
which ATT&CK techniques the AnalystAIPack skills cover. Each technique's **score** is the number
of skills that reference it, and its **comment** lists those skills.
Regenerate after adding or editing skills:
```bash
python tools/build-navigator-layer.py
```
### Viewing the layer
1. Open the [ATT&CK Navigator](https://mitre-attack.github.io/attack-navigator/).
2. Choose **Open Existing Layer → Upload from local**.
3. Select `mappings/attack-navigator-layer.json`.
Techniques are shaded by how many skills cover them (light → dark). Hover a technique to see
the contributing skills.
> ATT&CK is © The MITRE Corporation and used under its terms. The pinned `versions.attack`
> field in the layer records the ATT&CK release this layer targets.

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# Optional dependencies for AnalystAIPack skill scripts.
#
# Every scripts/analyst.py runs on the Python standard library alone and degrades gracefully
# when these are absent (reporting reduced capability rather than crashing). Install them to
# unlock full functionality for the skills noted below.
#
# pip install -r requirements-optional.txt
#
# Core runtime: Python 3.10+ standard library only — no required third-party packages.
pefile # PE import/export, entropy, hashing, diffing (analyzing-pe-imports-and-exports,
# performing-static-pe-analysis, measuring-section-entropy-to-detect-packing,
# hashing-and-fingerprinting-files, diffing-malware-samples-to-find-changes)
capstone # Disassembly for shellcode RE (reverse-engineering-shellcode)
unicorn # CPU emulation (emulating-shellcode-with-unicorn, reverse-engineering-shellcode)
r2pipe # radare2/rizin automation (automating-analysis-with-r2pipe); needs r2 or rizin installed
pycdlib # ISO directory listing (analyzing-malicious-iso-and-container-files)
ssdeep # Fuzzy hashing (hashing-and-fingerprinting-files, diffing-malware-samples-to-find-changes)
python-tlsh # TLSH fuzzy hashing (hashing-and-fingerprinting-files)
yara-python # YARA scanning (scanning-samples-with-yara)
# Not pip-installable (commercial / external tooling), required only for their specific skill:
# binaryninja -> reverse-engineering-binaries-with-binary-ninja (licensed Binary Ninja API)
# radare2 / rizin native binary -> automating-analysis-with-r2pipe,
# reverse-engineering-binaries-with-radare2-rizin
# Ghidra -> scripting-ghidra-headless-analysis, reverse-engineering-binaries-with-ghidra
# Volatility 3 -> detecting-process-injection-in-memory, analyzing-malware-in-memory-with-volatility3

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-android-dex-malware
description: 'Reverses Android malware: unpacking APKs, decompiling DEX bytecode to readable
Java, auditing the manifest for abused permissions and components, and locating dynamically
loaded or native payloads. Activates for requests to analyze an APK, decompile DEX, or
investigate a suspicious Android app.'
domain: cybersecurity
subdomain: reverse-engineering
tags:
- reverse-engineering
- android
- dex
- apk
- mobile
- decompilation
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1059
- T1406
- T1577
d3fend:
- D3-FCR
- D3-DA
references:
- 'Android DEX bytecode format — https://source.android.com/docs/core/runtime/dex-format'
- 'jadx DEX-to-Java decompiler — https://github.com/skylot/jadx'
---
# Analyzing Android DEX Malware
## When to Use
- You have a suspicious `.apk` (or bare `.dex`) and need to understand its behavior.
- You need to audit the manifest for dangerous permissions, exported components, and the
declared entry points.
- The app loads code dynamically (`DexClassLoader`) or ships a native `.so` you must locate.
**Do not use** this workflow for iOS apps — DEX/APK tooling does not apply to Mach-O/IPA.
## Prerequisites
- jadx (or apktool + a decompiler) and `unzip`; an Android emulator/sandbox for dynamic runs.
- `aapt`/manifest parsing for permissions and components.
## Workflow
### Step 1: Unpack the APK
An APK is a ZIP. Extract and inventory the DEX files, native libraries, and assets:
```bash
python scripts/analyst.py inspect sample.apk
```
```text
dex : classes.dex, classes2.dex
native : lib/arm64-v8a/libpayload.so
assets : assets/config.enc (possible encrypted payload)
manifest : AndroidManifest.xml (binary)
```
### Step 2: Audit the manifest
Decode `AndroidManifest.xml` and review requested permissions (SMS, accessibility, device
admin, `REQUEST_INSTALL_PACKAGES`), exported components, and the launcher/`BOOT_COMPLETED`
receivers.
### Step 3: Decompile DEX
Run jadx to recover Java. Start at the launcher activity and any `BroadcastReceiver`/`Service`
declared in the manifest.
### Step 4: Find dynamic and native code
Search for `DexClassLoader`/`loadDex`, asset decryption, and `System.loadLibrary`. Dump and
recurse on dynamically loaded DEX; analyze native `.so` separately if needed.
### Step 5: Extract behavior and IOCs
Recover C2 URLs, overlay/accessibility abuse, SMS interception, and config; map to ATT&CK for
mobile in the report.
## Validation
- Every DEX and native library in the APK is accounted for.
- Dangerous permissions are tied to concrete code paths (not just declared).
- Dynamically loaded payloads are dumped and analyzed, not just noted.
## Pitfalls
- Reading only `classes.dex` and missing `classes2.dex`/`classes3.dex`.
- Trusting the manifest alone; behavior may hide behind dynamic loading.
- Ignoring encrypted assets that become the real payload at runtime.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the APK inspector.
- Android DEX format and jadx (linked in frontmatter).

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# API Reference: Analyzing Android DEX Malware
## Overview
`scripts/analyst.py` treats an APK as a ZIP and inventories its DEX files, native libraries,
assets, and manifest presence, flagging multidex and likely encrypted payload assets. It does
not decompile or execute — use jadx/apktool for decompilation.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | zipfile, file IO |
## External tools
| Tool | Purpose |
|------|---------|
| jadx | Decompile DEX bytecode to Java |
| apktool | Decode resources and binary AndroidManifest.xml |
| Android emulator | Dynamic analysis of dynamic/native payloads |
## Core functions
### `inspect(path)`
Returns the DEX list, native libraries, assets, manifest presence, multidex flag, and triage
notes.
## Usage
```bash
python scripts/analyst.py inspect sample.apk
```
## Sources
- Android DEX bytecode format — https://source.android.com/docs/core/runtime/dex-format
- APK packaging (ZIP container, AndroidManifest.xml) — Android documentation
- jadx DEX-to-Java decompiler — https://github.com/skylot/jadx

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#!/usr/bin/env python3
"""Inspect an APK (ZIP) and inventory its DEX, native libraries, and assets.
Lists the components relevant to triage — DEX files, native ``.so`` libraries, the manifest,
and notable assets — without decompiling or executing anything. Use jadx/apktool for the
actual decompilation step.
"""
from __future__ import annotations
import argparse
import json
import sys
import zipfile
from pathlib import Path
def inspect(path: str) -> dict:
if not zipfile.is_zipfile(path):
return {"error": "not a ZIP/APK"}
dex, native, assets, other = [], [], [], []
with zipfile.ZipFile(path) as zf:
for name in zf.namelist():
if name.endswith(".dex"):
dex.append(name)
elif name.startswith("lib/") and name.endswith(".so"):
native.append(name)
elif name.startswith("assets/"):
assets.append(name)
elif name == "AndroidManifest.xml":
other.append(name)
return {
"dex": sorted(dex),
"native_libs": sorted(native),
"assets": sorted(assets),
"has_manifest": "AndroidManifest.xml" in other,
"multidex": len(dex) > 1,
"notes": _notes(dex, native, assets),
}
def _notes(dex, native, assets) -> list[str]:
notes = []
if len(dex) > 1:
notes.append("multidex: analyze all classesN.dex")
if native:
notes.append("native libraries present: analyze .so separately")
enc = [a for a in assets if a.endswith((".enc", ".dat", ".bin"))]
if enc:
notes.append(f"possible encrypted payload assets: {enc}")
return notes
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Inspect an APK")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("inspect")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(inspect(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-api-call-traces
description: 'Analyzes API call traces from a sandbox or API monitor (JSON) to group calls by
category, reconstruct high-level behaviors (process injection, file drops, network, crypto), and
flag suspicious call sequences. Activates for requests to analyze an API trace, interpret sandbox
API logs, or identify behavior from Win32/Native API calls.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- dynamic-analysis
- api-trace
- winapi
- sandbox
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1106
- T1055
- T1027
d3fend:
- D3-PSA
- D3-SDA
references:
- 'MITRE ATT&CK T1106 Native API — https://attack.mitre.org/techniques/T1106/'
- 'Windows API Index — https://learn.microsoft.com/windows/win32/apiindex/windows-api-list'
---
# Analyzing API Call Traces
## When to Use
- You have a JSON API trace (CAPE/Cuckoo "apistats"/calls, API Monitor export) from a detonation.
- You need to translate raw API calls into high-level behaviors and flag suspicious sequences
(e.g., `VirtualAllocEx` → `WriteProcessMemory` → `CreateRemoteThread`).
**Do not use** this as a substitute for detonation — it consumes an existing trace produced in an
isolated sandbox.
## Prerequisites
- A JSON API trace with at least an API name per call (and optionally arguments).
## Safety & Handling
- Consume traces only; never execute the sample to generate one outside a sandbox.
## Workflow
### Step 1: Group calls by category
```bash
python scripts/analyst.py classify trace.json
```
Buckets calls into process, memory, file, registry, network, crypto, and synchronization.
### Step 2: Detect behavior signatures
Matches ordered API sequences that indicate techniques — remote injection, hollowing,
self-deletion, dynamic API resolution (`LoadLibrary`/`GetProcAddress` bursts).
### Step 3: Prioritize and report
Rank detected behaviors by severity and map each to ATT&CK.
## Validation
- Each API call is categorized; unknown APIs are reported, not dropped silently.
- Injection-style sequences are detected only when the ordered prerequisites are present.
- Detected behaviors map to ATT&CK techniques.
## Pitfalls
- Flagging individual benign APIs out of context — the sequence matters.
- Assuming call order in the JSON equals execution order without a timestamp/index.
- Ignoring failed calls (non-zero error) that never actually executed the behavior.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the classifier.
- ATT&CK T1106 and the Windows API Index (linked in frontmatter).

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# API Reference: Analyzing API Call Traces
## Overview
`scripts/analyst.py` classifies a JSON API trace into behavioral categories and detects ordered
API sequences indicative of process injection, hollowing, dynamic API resolution, and crypto
routines. It parses JSON only and executes nothing.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | json, argparse |
## Accepted input shapes
- A list of strings (API names) or call objects with `api`/`name`/`function` keys.
- A dict containing `calls`, `apis`, or a CAPE-style `apistats` mapping.
## Core functions
### `classify(path)`
Returns total call count, per-category counts, detected behaviors (with ATT&CK IDs), and unknown
APIs.
## Sequence signatures
`VirtualAllocEx → WriteProcessMemory → CreateRemoteThread` (T1055.002), hollowing (T1055.012),
`LoadLibrary → GetProcAddress` (T1027.007), `CryptAcquireContext → CryptDecrypt` (T1027). Matching
respects order via a sliding cursor.
## Usage
```bash
python scripts/analyst.py classify trace.json
```
## Sources
- MITRE ATT&CK T1106 Native API; T1055 Process Injection — https://attack.mitre.org/
- Windows API Index — https://learn.microsoft.com/windows/win32/apiindex/windows-api-list
- CAPE/Cuckoo report `behavior.apistats` structure (public sandbox schema)

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#!/usr/bin/env python3
"""Classify a JSON API call trace and detect behavior signatures.
Accepts a flexible JSON trace: a list of call objects, or a dict with a "calls"/"apis" list, or a
CAPE-style apistats mapping. Categorizes API names and matches ordered sequences indicative of
process injection, dynamic API resolution, and self-deletion. Parses JSON only; executes nothing.
"""
from __future__ import annotations
import argparse
import json
import sys
CATEGORIES = {
"process": ["createprocess", "openprocess", "createremotethread", "ntcreatethreadex",
"resumethread", "terminateprocess"],
"memory": ["virtualalloc", "virtualallocex", "virtualprotect", "writeprocessmemory",
"readprocessmemory", "ntmapviewofsection", "ntunmapviewofsection"],
"file": ["createfile", "writefile", "readfile", "deletefile", "copyfile", "movefile"],
"registry": ["regopenkey", "regsetvalue", "regcreatekey", "regqueryvalue"],
"network": ["internetopen", "internetconnect", "httpsendrequest", "wininet", "winhttp",
"send", "recv", "connect", "getaddrinfo"],
"crypto": ["cryptencrypt", "cryptdecrypt", "cryptacquirecontext", "bcryptencrypt",
"cryptstringtobinary"],
"resolve": ["loadlibrary", "getprocaddress", "ldrloaddll", "ldrgetprocedureaddress"],
"sync": ["createmutex", "openmutex", "createevent", "sleep", "waitforsingleobject"],
}
SIGNATURES = [
("remote_process_injection", ["virtualallocex", "writeprocessmemory", "createremotethread"],
"T1055.002"),
("process_hollowing", ["createprocess", "ntunmapviewofsection", "writeprocessmemory"],
"T1055.012"),
("dynamic_api_resolution", ["loadlibrary", "getprocaddress"], "T1027.007"),
("crypto_routine", ["cryptacquirecontext", "cryptdecrypt"], "T1027"),
]
def _extract_calls(obj) -> list[str]:
if isinstance(obj, list):
out = []
for c in obj:
if isinstance(c, str):
out.append(c)
elif isinstance(c, dict):
out.append(c.get("api") or c.get("name") or c.get("function") or "")
return [c for c in out if c]
if isinstance(obj, dict):
for key in ("calls", "apis", "apistats"):
if key in obj:
val = obj[key]
if isinstance(val, dict):
return list(val.keys())
return _extract_calls(val)
return []
def _category(api: str) -> str:
low = api.lower()
for cat, names in CATEGORIES.items():
if any(n in low for n in names):
return cat
return "other"
def classify(path: str) -> dict:
data = json.loads(open(path, encoding="utf-8-sig").read())
calls = _extract_calls(data)
lowered = [c.lower() for c in calls]
cats: dict[str, int] = {}
unknown = set()
for api in calls:
cat = _category(api)
cats[cat] = cats.get(cat, 0) + 1
if cat == "other":
unknown.add(api)
detected = []
for name, seq, tech in SIGNATURES:
pos = 0
for needle in seq:
found = next((i for i in range(pos, len(lowered)) if needle in lowered[i]), -1)
if found == -1:
break
pos = found + 1
else:
detected.append({"behavior": name, "attack": tech})
return {
"total_calls": len(calls),
"categories": cats,
"behaviors": detected,
"unknown_apis": sorted(unknown)[:50],
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Classify an API call trace")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("classify")
p.add_argument("trace")
args = parser.parse_args(argv)
print(json.dumps(classify(args.trace), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-authenticode-signatures
description: 'Analyzes Windows Authenticode signatures on PE files: checking for a signature,
reading the signer certificate chain, detecting revoked/expired/stolen certs, and recognizing
signature-stripping and catalog-signing abuse. Activates for requests to analyze a code
signature, verify Authenticode, or assess signer trust on a PE.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- authenticode
- code-signing
- pe
- certificates
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1553.002
- T1036.001
d3fend:
- D3-FCR
references:
- 'Windows Authenticode PE signature format — https://learn.microsoft.com/windows-hardware/drivers/install/authenticode'
- 'Microsoft PE/COFF specification (security directory) — https://learn.microsoft.com/windows/win32/debug/pe-format'
---
# Analyzing Authenticode Signatures
## When to Use
- You need to know whether a PE is signed and whether the signer is trustworthy.
- You suspect a stolen/abused code-signing certificate or a forged/invalid signature.
- You are distinguishing legitimately signed software from signed malware.
**Do not use** "is signed" as "is safe" — adversaries use stolen certs, abuse catalog signing,
and append invalid signatures; a valid signature only attests the signer, not benignity.
## Prerequisites
- Access to the PE security directory (Python `struct`/`pefile`); on Windows, `signtool`/PowerShell
`Get-AuthenticodeSignature` for authoritative verification.
## Safety & Handling
- Parse the signature statically; never execute the sample.
- Keep the sample password-protected at rest and reference it by hash.
## Workflow
### Step 1: Check for a signature
Read the PE security directory (certificate table). Absent entry = unsigned; present = embedded
PKCS#7. Note that valid signing can also be via a catalog file (not embedded).
```bash
python scripts/analyst.py check sample.exe
```
### Step 2: Extract signer details
Parse the PKCS#7 to read the signer certificate: subject, issuer, validity dates, and serial.
Cross-reference against known-abused/stolen certs.
### Step 3: Verify, do not just read
On Windows, verify the signature cryptographically and check revocation
(`Get-AuthenticodeSignature`). A present-but-invalid signature is a strong red flag.
### Step 4: Assess trust in context
Weigh signer reputation, certificate age, and whether the signed file's behavior matches the
purported publisher. Treat anomalies (recently issued cert, mismatched publisher) as suspicious.
## Validation
- Signature presence is determined from the security directory, and verified (not just read) on
Windows.
- Signer certificate details are extracted and checked against abuse intel.
- The trust conclusion accounts for revocation/validity, not mere presence.
## Pitfalls
- Equating a present signature with safety; stolen-cert malware is common.
- Missing catalog-signed files by only checking embedded signatures.
- Reading the signature without cryptographically verifying it.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the signature checker.
- Authenticode format and the PE/COFF security directory (linked in frontmatter).

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# API Reference: Analyzing Authenticode Signatures
## Overview
`scripts/analyst.py` parses the PE security data directory (index 4) to detect an embedded PKCS#7
Authenticode signature (WIN_CERTIFICATE), reporting presence, size, revision, and type. It is a
dependency-free presence check; cryptographic verification requires OS tooling. It parses bytes
statically and never executes the sample.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | struct parsing, file IO |
## External tools
| Tool | Purpose |
|------|---------|
| signtool / Get-AuthenticodeSignature | Cryptographically verify the signature and revocation |
## Core functions
### `check(path)`
Returns whether an embedded signature exists and the WIN_CERTIFICATE header fields.
## Usage
```bash
python scripts/analyst.py check sample.exe
```
## Sources
- Windows Authenticode PE signature format —
https://learn.microsoft.com/windows-hardware/drivers/install/authenticode
- PE/COFF security data directory (index 4, file-offset semantics) —
https://learn.microsoft.com/windows/win32/debug/pe-format
- Stolen/abused signing certificates (T1553.002), MITRE ATT&CK

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#!/usr/bin/env python3
"""Check a PE for an embedded Authenticode signature and summarize the certificate blob.
Parses the PE optional-header security data directory (index 4) to detect an embedded PKCS#7
signature (WIN_CERTIFICATE), reporting its presence, size, and revision/type. Deep certificate
parsing and cryptographic verification require a PKCS#7 library / OS tooling; this gives a fast,
dependency-free presence check. It parses bytes statically and never executes the sample.
"""
from __future__ import annotations
import argparse
import json
import struct
import sys
from pathlib import Path
WIN_CERT_TYPE = {0x0001: "X509", 0x0002: "PKCS_SIGNED_DATA", 0x0003: "RESERVED",
0x0004: "TS_STACK_SIGNED"}
def check(path: str) -> dict:
data = Path(path).read_bytes()
if data[:2] != b"MZ":
return {"error": "not a PE"}
e_lfanew = struct.unpack_from("<I", data, 0x3C)[0]
if data[e_lfanew:e_lfanew + 4] != b"PE\x00\x00":
return {"error": "no PE signature"}
opt = e_lfanew + 24
magic = struct.unpack_from("<H", data, opt)[0]
pe32_plus = magic == 0x20B
dd_offset = opt + (112 if pe32_plus else 96)
sec_dir = dd_offset + 4 * 8 # security directory is index 4
rva, size = struct.unpack_from("<II", data, sec_dir)
if not rva or not size:
return {"signed_embedded": False,
"note": "no embedded signature (may still be catalog-signed)"}
# For the security directory, 'rva' is a file offset (not an RVA)
dwlen, wrev, wtype = struct.unpack_from("<IHH", data, rva)
return {
"signed_embedded": True,
"certificate_table_offset": rva,
"certificate_table_size": size,
"win_cert_length": dwlen,
"revision": hex(wrev),
"type": WIN_CERT_TYPE.get(wtype, hex(wtype)),
"note": "presence only; verify cryptographically with signtool / "
"Get-AuthenticodeSignature",
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Check Authenticode signature presence")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("check")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(check(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-banking-trojan-webinjects
description: 'Analyzes banking trojan webinject configurations to extract targeted institutions,
injected JavaScript/HTML, and data-theft hooks, mapping the fraud workflow. Activates for
requests to analyze banking trojan webinjects, parse a webinject config, or map targeted banks
and credential-theft injections.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- banking-trojan
- webinject
- fraud
- credential-theft
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1185
- T1557
- T1056.001
d3fend:
- D3-SDA
- D3-DA
references:
- 'MITRE ATT&CK T1185 Browser Session Hijacking — https://attack.mitre.org/techniques/T1185/'
- 'Zeus/SpyEye webinject format (public malware research) — https://attack.mitre.org/software/S0259/'
---
# Analyzing Banking Trojan Webinjects
## When to Use
- You have a banking trojan webinject config (Zeus/Gozi/Dridex-style `set_url`/`data_before`/
`data_inject` blocks) and want to extract targeted institutions and injected code.
- You are mapping the web-fraud workflow (form-grabbing, fake fields, MFA interception).
**Do not use** this to deploy or test injects against live banking sites. This skill parses the
config statically and executes nothing.
## Prerequisites
- The decoded webinject config text (decrypt the config first if needed).
## Safety & Handling
- Treat injected JS/HTML as malicious; do not render it. Defang any exfil URLs.
## Workflow
### Step 1: Parse webinject blocks
```bash
python scripts/analyst.py parse webinjects.txt
```
Parses `set_url`/`data_before`/`data_inject`/`data_after` blocks into structured entries with the
targeted URL pattern and the injected snippet.
### Step 2: Extract targets and exfil endpoints
List targeted institution URL patterns and any exfil endpoints referenced by the injected code.
### Step 3: Classify the injection technique
Identify form-grabbing, fake additional fields (SSN, PIN, card), and MFA/OTP interception from the
injected markup/JS.
### Step 4: Defang and report
Defang URLs and summarize targeted banks and theft techniques.
## Validation
- Each webinject block is parsed into target URL + injection.
- Exfil endpoints in injected code are extracted and defanged.
- Injection techniques are classified from the snippet content.
## Pitfalls
- Encrypted/packed configs needing decryption before parsing.
- Wildcard URL patterns matching broadly — note the scope.
- Obfuscated injected JS hiding the true exfil endpoint.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the parser.
- ATT&CK T1185 and webinject format references (linked in frontmatter).

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# API Reference: Analyzing Banking Trojan Webinjects
## Overview
`scripts/analyst.py` parses Zeus/SpyEye-style webinject configs (`set_url` + `data_before`/
`data_inject`/`data_after` blocks), associates each target URL pattern with its injected snippet,
classifies the injection technique, and extracts/defangs exfil endpoints. It parses config text
only, executes nothing, and never renders injected code.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | regex, JSON, file IO |
## Core functions
### `parse(path)`
Returns webinject count, targeted URL patterns, and per-entry technique/exfil/preview details.
## Technique classes
- form_grabbing (onsubmit/form hooks)
- fake_fields (SSN/PIN/card/CVV/DOB additions)
- mfa_interception (OTP/2FA/token capture)
- balance_manipulation (innerHTML/balance rewriting)
## Webinject format
`set_url <pattern> <flags>` followed by `data_before … data_end`, `data_inject … data_end`,
`data_after … data_end`.
## Usage
```bash
python scripts/analyst.py parse webinjects.txt
```
## Sources
- MITRE ATT&CK T1185 Browser Session Hijacking — https://attack.mitre.org/techniques/T1185/
- Zeus/SpyEye webinject grammar (set_url/data_before/data_inject) from public malware research
- Web-fraud injection techniques (form-grab, fake fields, OTP capture) from banking-trojan analyses

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#!/usr/bin/env python3
"""Parse banking trojan webinject configs (Zeus/SpyEye-style) into structured entries.
Parses set_url / data_before / data_inject / data_after blocks, extracts the targeted URL pattern
and injected snippet, classifies the injection technique, and extracts/defangs exfil endpoints.
Parses config text only; executes nothing and does not render injected code.
"""
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
RE_SETURL = re.compile(r"set_url\s+(\S+)(?:\s+(\S+))?", re.I)
RE_BLOCK = re.compile(
r"data_(before|inject|after)\b(.*?)data_end", re.I | re.S)
RE_URL = re.compile(r"https?://[^\s\"'<>]+", re.I)
TECHNIQUE_HINTS = {
"form_grabbing": ["onsubmit", "form", "addeventlistener", "password"],
"fake_fields": ["ssn", "social security", "pin", "card number", "cvv", "mother",
"date of birth", "atm"],
"mfa_interception": ["otp", "one-time", "token", "2fa", "verification code", "sms code"],
"balance_manipulation": ["balance", "innerhtml", "replace"],
}
def parse(path: str) -> dict:
text = Path(path).read_text(encoding="utf-8-sig", errors="replace")
entries = []
# Split on set_url boundaries to associate each target with its following blocks.
chunks = re.split(r"(?=set_url\s)", text, flags=re.I)
for chunk in chunks:
m = RE_SETURL.search(chunk)
if not m:
continue
target = m.group(1)
inject = ""
for bname, body in RE_BLOCK.findall(chunk):
if bname.lower() == "inject":
inject = body.strip()
low = inject.lower()
techniques = [t for t, needles in TECHNIQUE_HINTS.items()
if any(n in low for n in needles)]
exfil = sorted({u.replace("http", "hxxp").replace(".", "[.]")
for u in RE_URL.findall(inject)})
entries.append({
"target_url": target,
"techniques": techniques,
"exfil_endpoints": exfil,
"inject_preview": inject[:160],
})
targets = sorted({e["target_url"] for e in entries})
return {
"webinject_count": len(entries),
"targeted_url_patterns": targets,
"entries": entries,
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Parse banking trojan webinjects")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("parse")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(parse(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-compiled-python-malware
description: 'Analyzes Python-based malware packaged as PyInstaller/py2exe executables by detecting
the packer, locating the embedded archive, and identifying bundled .pyc modules for extraction and
decompilation. Activates for requests to analyze compiled Python malware, unpack a PyInstaller
binary, or extract pyc modules from a frozen Python executable.'
domain: cybersecurity
subdomain: reverse-engineering
tags:
- reverse-engineering
- python
- pyinstaller
- pyc
- unpacking
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1027
- T1059.006
- T1587.001
d3fend:
- D3-SDA
- D3-DA
references:
- 'PyInstaller archive format (CArchive/PKG) — https://pyinstaller.org/en/stable/advanced-topics.html'
- 'Python bytecode (dis module / .pyc magic) — https://docs.python.org/3/library/dis.html'
---
# Analyzing Compiled Python Malware
## When to Use
- You have an executable that is actually a frozen Python app (PyInstaller, py2exe, cx_Freeze) and
need to identify the packer and locate the embedded Python modules.
- You want to extract `.pyc` files for decompilation.
**Do not use** this to run the executable — it identifies and locates the embedded archive
statically. Decompile extracted `.pyc` in an isolated environment.
## Prerequisites
- The frozen executable (read inertly).
## Safety & Handling
- Read bytes statically; treat extracted modules as malicious until reviewed.
## Workflow
### Step 1: Detect the packer
```bash
python scripts/analyst.py detect sample.exe
```
Looks for PyInstaller markers (`pyi-`, `PYZ-00.pyz`, the `MEI` CArchive cookie `MEI\014\013\012\013\016`),
py2exe (`PYTHONSCRIPT`, `zipfile.zip`), and embedded `python3x.dll` references.
### Step 2: Locate the embedded archive
Find the CArchive cookie near the end of the file and report the offset and the embedded Python
version string (`python3.x`).
### Step 3: Extract and decompile
Use a PyInstaller extractor to dump the archive, then decompile `.pyc` (matching the detected
Python version) for source recovery.
### Step 4: Analyze the source
Review the recovered Python for C2, persistence, and capability; map to ATT&CK.
## Validation
- The packer is identified by its specific marker, not just the presence of Python strings.
- The CArchive cookie offset and Python version are reported when PyInstaller is present.
- Findings distinguish the bootloader stub from the embedded Python payload.
## Pitfalls
- `.pyc` version mismatch breaking decompilation — match the interpreter version.
- Stripped/obfuscated bytecode (e.g., custom magic) needing header repair before decompiling.
- Encrypted PYZ archives (PyInstaller `--key`) requiring the key.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the detector.
- PyInstaller archive and Python bytecode references (linked in frontmatter).

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# API Reference: Analyzing Compiled Python Malware
## Overview
`scripts/analyst.py` detects frozen-Python packers (PyInstaller, py2exe, cx_Freeze) by their markers,
locates the PyInstaller CArchive magic cookie, and extracts embedded Python version strings. It
reads bytes statically and executes nothing.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | regex, JSON, file IO |
## Core functions
### `detect(path)`
Returns the detected packer, PyInstaller cookie offset, and Python version strings, plus the
recommended next step.
## Markers
- PyInstaller: `_MEIPASS`, `PYZ-00.pyz`, `pyiboot01_bootstrap`, CArchive cookie `MEI\\014\\013\\012\\013\\016`.
- py2exe: `PYTHONSCRIPT`.
- cx_Freeze: `cx_Freeze` strings.
## Usage
```bash
python scripts/analyst.py detect sample.exe
```
## Sources
- PyInstaller archive format (CArchive/PKG, MEI cookie) — https://pyinstaller.org/en/stable/advanced-topics.html
- Python bytecode / .pyc magic (dis module) — https://docs.python.org/3/library/dis.html
- pyinstxtractor extraction workflow (public PyInstaller-unpacking tooling)

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#!/usr/bin/env python3
"""Detect frozen-Python packers and locate the embedded archive.
Scans an executable for PyInstaller, py2exe, and cx_Freeze markers, locates the PyInstaller
CArchive magic cookie, and extracts embedded Python version strings. Reads bytes statically;
executes nothing.
"""
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
PYI_COOKIE = b"MEI\014\013\012\013\016"
PYI_MARKERS = [b"pyi-windows-manifest-filename", b"PYZ-00.pyz", b"pyiboot01_bootstrap",
b"_MEIPASS"]
PY2EXE_MARKERS = [b"PYTHONSCRIPT", b"py2exe"]
CXFREEZE_MARKERS = [b"cx_Freeze", b"cx_freeze__init__"]
RE_PYVER = re.compile(rb"python(\d)\.?(\d{1,2})")
RE_PYDLL = re.compile(rb"python(\d)(\d{1,2})\.dll", re.I)
def detect(path: str) -> dict:
data = Path(path).read_bytes()
pyi = any(m in data for m in PYI_MARKERS) or PYI_COOKIE in data
py2exe = any(m in data for m in PY2EXE_MARKERS)
cxfreeze = any(m in data for m in CXFREEZE_MARKERS)
cookie_off = data.rfind(PYI_COOKIE)
versions = sorted({f"{m.group(1).decode()}.{m.group(2).decode()}"
for m in RE_PYVER.finditer(data)})
dll_versions = sorted({f"{m.group(1).decode()}.{m.group(2).decode()}"
for m in RE_PYDLL.finditer(data)})
packer = "PyInstaller" if pyi else "py2exe" if py2exe else \
"cx_Freeze" if cxfreeze else "unknown"
return {
"is_frozen_python": pyi or py2exe or cxfreeze,
"packer": packer,
"pyinstaller_cookie_offset": cookie_off if cookie_off != -1 else None,
"python_versions": versions[:5],
"python_dll_versions": dll_versions[:5],
"next_step": "extract the archive (e.g. pyinstxtractor) then decompile matching .pyc",
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Detect frozen-Python packers")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("detect")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(detect(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-cryptominer-malware
description: 'Analyzes cryptojacking/coinminer malware by extracting mining pool endpoints
(stratum), wallet addresses, algorithm and miner identifiers (XMRig and forks), and persistence
or resource-control settings from static strings and config. Activates for requests to analyze a
cryptominer, extract mining pool and wallet IOCs, or identify coinminer configuration.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- cryptominer
- cryptojacking
- xmrig
- resource-hijacking
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1496
- T1543
- T1027
d3fend:
- D3-PSA
- D3-DA
references:
- 'MITRE ATT&CK T1496 Resource Hijacking — https://attack.mitre.org/techniques/T1496/'
- 'XMRig configuration documentation — https://xmrig.com/docs/miner/config'
---
# Analyzing Cryptominer Malware
## When to Use
- You have a suspected coinminer (often an XMRig fork) and need to extract its pool, wallet,
algorithm, and resource-control configuration for IOCs and attribution.
- You are confirming a host's high CPU/GPU usage is malicious mining.
**Do not use** this to run the miner — extract its configuration statically.
## Prerequisites
- The miner sample or its extracted config/strings, read inertly.
## Safety & Handling
- Read bytes statically; defang pool endpoints; never start the miner.
## Workflow
### Step 1: Extract mining configuration
```bash
python scripts/analyst.py profile sample.bin
```
Extracts `stratum+tcp://`/`stratum+ssl://` pool URLs, wallet addresses (Monero and common
formats), algorithm names (`rx/0`, `cn/r`, etc.), and the XMRig/miner identifier strings.
### Step 2: Identify resource and stealth controls
Surface `max-cpu-usage`, `--background`, idle-detection, and `donate-level` settings, plus
watchdog/persistence indicators.
### Step 3: Build IOCs
Record defanged pool endpoints, wallet addresses, and the miner family for detection and takedown
referral.
### Step 4: Document
Summarize the campaign's pool/wallet and the resource-control posture.
## Validation
- Pool URLs use a recognized `stratum` scheme and resolve to plausible hosts.
- Wallet addresses match a known coin's format/length.
- The miner family is identified from its config keys/strings.
## Pitfalls
- Legitimate mining software shares the same config keys — context (deployment, stealth) matters.
- Encrypted/packed config requiring decryption first.
- Proxy pools masking the true upstream pool.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the profiler.
- ATT&CK T1496 and the XMRig config reference (linked in frontmatter).

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# API Reference: Analyzing Cryptominer Malware
## Overview
`scripts/analyst.py` extracts a coinminer's configuration from static strings: stratum pool
endpoints, wallet addresses (Monero/Bitcoin/Ethereum formats), algorithm identifiers, miner
family strings, and resource-control/stealth keys. It reads bytes statically, defangs pool
endpoints, and executes nothing.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | regex, JSON, file IO |
## Core functions
### `profile(path)`
Returns pool endpoints, wallet addresses (by coin), algorithms, miner family, resource controls,
and a cryptominer likelihood flag.
## Extracted indicators
- **Pools**: `stratum+tcp://` / `stratum+ssl://`.
- **Wallets**: Monero (`4…`, 95 chars), Bitcoin (legacy + bech32), Ethereum (`0x…40 hex`).
- **Algorithms**: `rx/0`, `cn/r`, `randomx`, `kawpow`, `ethash`, etc.
- **Resource controls**: `max-cpu-usage`, `donate-level`, `background`, `idle`.
## Usage
```bash
python scripts/analyst.py profile sample.bin
```
## Sources
- MITRE ATT&CK T1496 Resource Hijacking — https://attack.mitre.org/techniques/T1496/
- XMRig configuration keys — https://xmrig.com/docs/miner/config
- Coin address formats from each coin's public address specification

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#!/usr/bin/env python3
"""Profile a cryptominer's configuration from static strings.
Extracts stratum pool endpoints, wallet addresses, algorithm identifiers, miner family strings,
and resource-control/stealth settings. Reads bytes statically, defangs pool endpoints, and
executes nothing.
"""
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
ASCII_RE = re.compile(rb"[\x20-\x7e]{4,}")
RE_STRATUM = re.compile(r"stratum\+(?:tcp|ssl)://[^\s\"'<>]+", re.I)
RE_MONERO = re.compile(r"\b4[0-9AB][0-9A-Za-z]{93}\b")
RE_BTC = re.compile(r"\b(?:bc1[0-9a-z]{20,60}|[13][a-km-zA-HJ-NP-Z1-9]{25,34})\b")
RE_ETH = re.compile(r"\b0x[a-fA-F0-9]{40}\b")
ALGOS = ["rx/0", "rx/wow", "cn/r", "cn/2", "cn-heavy", "argon2", "kawpow", "ethash", "randomx",
"cryptonight"]
MINER_IDS = ["xmrig", "xmr-stak", "minerd", "cpuminer", "nbminer", "phoenixminer", "t-rex",
"lolminer", "nanominer"]
RESOURCE_KEYS = ["max-cpu-usage", "cpu-priority", "donate-level", "background", "max-threads-hint",
"huge-pages", "idle", "watchdog"]
def _strings(data: bytes) -> str:
return "\n".join(m.decode("latin-1", "ignore") for m in ASCII_RE.findall(data))
def _defang(s: str) -> str:
return s.replace("stratum", "stratum-x").replace("://", "[://]").replace(".", "[.]")
def profile(path: str) -> dict:
blob = _strings(Path(path).read_bytes())
low = blob.lower()
pools = sorted({_defang(p) for p in RE_STRATUM.findall(blob)})
wallets = {
"monero": sorted(set(RE_MONERO.findall(blob)))[:10],
"bitcoin": sorted(set(RE_BTC.findall(blob)))[:10],
"ethereum": sorted(set(RE_ETH.findall(blob)))[:10],
}
wallets = {k: v for k, v in wallets.items() if v}
algos = sorted({a for a in ALGOS if a in low})
miners = sorted({m for m in MINER_IDS if m in low})
resource = sorted({r for r in RESOURCE_KEYS if r in low})
return {
"pool_endpoints": pools,
"wallet_addresses": wallets,
"algorithms": algos,
"miner_family": miners,
"resource_controls": resource,
"is_likely_cryptominer": bool(pools or (miners and algos)),
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Profile a cryptominer")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("profile")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(profile(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-dotnet-malware-internals
description: 'Reverses .NET/managed malware: decompiling MSIL back to C#, defeating common
.NET protectors and string encryptors, and tracing reflection-based loaders to recover the
real payload. Activates for requests to analyze a .NET sample, decompile MSIL, or unpack a
managed loader.'
domain: cybersecurity
subdomain: reverse-engineering
tags:
- reverse-engineering
- dotnet
- msil
- decompilation
- deobfuscation
- managed-code
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1027
- T1620
- T1055
d3fend:
- D3-FCR
- D3-DA
references:
- 'ECMA-335 Common Language Infrastructure (CIL/MSIL) — https://ecma-international.org/publications-and-standards/standards/ecma-335/'
- 'ILSpy decompiler — https://github.com/icsharpcode/ILSpy'
---
# Analyzing .NET Malware Internals
## When to Use
- A sample is a managed (.NET) assembly — confirmed by a CLR header / `mscoree` import or
`BSJB` metadata signature.
- You need readable C# from MSIL and want to defeat .NET-specific obfuscation.
- A loader uses reflection (`Assembly.Load`) to run an in-memory payload you must recover.
**Do not use** native disassembly workflows (Ghidra for x86) as the primary tool — managed
code decompiles far more cleanly with a .NET decompiler.
## Prerequisites
- ILSpy / dnSpyEx for decompilation and (with dnSpyEx) managed debugging.
- de4dot or equivalent for known protectors; familiarity with common .NET obfuscators.
## Workflow
### Step 1: Confirm it is managed
Check for the CLR runtime header and the `BSJB` metadata magic:
```bash
python scripts/analyst.py identify sample.exe
```
### Step 2: Decompile
Open in ILSpy/dnSpyEx and review the entry point, `Main`, and module initializer
(`<Module>.cctor`), which protectors often abuse.
### Step 3: Handle obfuscation
Recognize and undo common schemes:
- **String encryption** — a decryptor method called everywhere; run/trace it to recover
plaintext (de4dot can often static-decrypt).
- **Control-flow flattening** — follow the dispatcher state machine.
- **Proxy methods / renaming** — rely on decompiler analysis rather than names.
### Step 4: Trace reflection loaders
Find `Assembly.Load(byte[])` / `Activator.CreateInstance`; dump the byte array argument at
runtime (managed debugger breakpoint) to recover the real second-stage assembly, then recurse.
### Step 5: Analyze the payload
Decompile the recovered stage; extract C2, configuration, and capabilities for the report.
## Validation
- Decompiled C# is coherent (named or recovered) and the entry path is traced.
- Encrypted strings are recovered to plaintext.
- The reflection-loaded stage is dumped and itself decompiles.
## Pitfalls
- Treating the loader as the payload — managed malware is frequently multi-stage.
- Ignoring the module initializer where protectors install hooks.
- Static-decrypting strings when the scheme is runtime-keyed; debug and dump instead.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the CLR identifier.
- ECMA-335 and ILSpy (linked in frontmatter).

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# API Reference: Reverse Engineering .NET Malware
## Overview
`scripts/analyst.py` parses a PE to detect the CLR runtime header (COM descriptor data directory
index 14) and the `BSJB` metadata-root signature, confirming whether a sample is managed. It
parses bytes only and never executes the sample.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | struct parsing, file IO |
## External tools
| Tool | Purpose |
|------|---------|
| ILSpy / dnSpyEx | Decompile MSIL to C#; managed debugging |
| de4dot | Static deobfuscation of known protectors |
## Core functions
### `identify(path)`
Returns whether the sample is managed, whether the `BSJB` metadata signature is present, and
diagnostic notes (mscoree/_CorExeMain references).
## Usage
```bash
python scripts/analyst.py identify sample.exe
```
## Sources
- ECMA-335 Common Language Infrastructure (CIL/MSIL metadata, BSJB root) —
https://ecma-international.org/publications-and-standards/standards/ecma-335/
- PE COM descriptor data directory (index 14) — PE/COFF specification
- ILSpy decompiler — https://github.com/icsharpcode/ILSpy

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#!/usr/bin/env python3
"""Identify a .NET/managed assembly and surface basic CLR metadata signals.
Parses the PE far enough to detect the COM descriptor (CLR) data directory and searches for
the ``BSJB`` metadata-root signature. Static parsing only; the sample is never executed.
"""
from __future__ import annotations
import argparse
import json
import struct
import sys
from pathlib import Path
def identify(path: str) -> dict:
data = Path(path).read_bytes()
result = {"managed": False, "bsjb_metadata": False, "notes": []}
if data[:2] != b"MZ":
result["notes"].append("not a PE (no MZ)")
return result
e_lfanew = struct.unpack_from("<I", data, 0x3C)[0]
if data[e_lfanew:e_lfanew + 4] != b"PE\x00\x00":
result["notes"].append("no PE signature")
return result
coff = e_lfanew + 4
opt = coff + 20
magic = struct.unpack_from("<H", data, opt)[0]
pe32_plus = magic == 0x20B
# data directories start after the standard+windows optional header fields
dd_offset = opt + (112 if pe32_plus else 96)
# COM descriptor is data directory index 14
com_dir = dd_offset + 14 * 8
rva, size = struct.unpack_from("<II", data, com_dir)
if rva and size:
result["managed"] = True
result["notes"].append("CLR runtime header (COM descriptor) present")
if b"BSJB" in data:
result["bsjb_metadata"] = True
result["notes"].append("BSJB metadata root signature found")
if b"mscoree.dll" in data or b"_CorExeMain" in data:
result["notes"].append("mscoree / _CorExeMain reference")
result["managed"] = result["managed"] or result["bsjb_metadata"]
return result
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Identify managed .NET assembly")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("identify")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(identify(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-elf-binaries-on-linux
description: 'Statically analyzes Linux ELF malware: ELF header and sections, dynamic symbols
and imports, segment permissions, embedded strings, and packing indicators to infer
capability without execution. Activates for requests to analyze an ELF binary, Linux
malware, or shared object.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware
- static-analysis
- elf
- linux
- symbols
- entropy
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1027
- T1059.004
- T1129
d3fend:
- D3-FA
- D3-FCR
references:
- 'ELF specification (System V ABI) — https://refspecs.linuxfoundation.org/elf/elf.pdf'
- 'pyelftools — https://github.com/eliben/pyelftools'
---
# Analyzing ELF Binaries on Linux
## When to Use
- You have a Linux executable or shared object and need to assess it statically.
- You want to read the ELF header, segments, dynamic symbols, and strings to infer behavior.
- You are checking for static linking, stripped symbols, or packing.
**Do not use** symbol absence as proof of nothing — stripped or statically linked Go/Rust
binaries hide structure; switch to disassembly when static metadata is thin.
## Prerequisites
- `readelf`/`nm`/`strings` (binutils) or `pyelftools` (`pip install pyelftools`).
- The sample in neutralized form inside the lab.
## Safety & Handling
- Static only: parse the file; do not set it executable or run it.
## Workflow
### Step 1: Read the ELF header
```bash
python scripts/analyst.py analyze sample.elf
```
Note class (ELF32/64), endianness, type (EXEC/DYN/REL), machine (x86-64, ARM, MIPS), and
whether it is stripped.
### Step 2: Examine segments and section permissions
Writable+executable segments, or a single large segment, suggest packing or self-modifying
code. Compare `PT_LOAD` permissions against expectations.
### Step 3: Inspect dynamic symbols and needed libraries
Imported functions hint at capability: `socket`/`connect` (network), `ptrace` (anti-debug or
injection), `fork`/`execve` (process control), `crypt`/`EVP_*` (encryption).
### Step 4: Detect static linking and packing
Static binaries lack a dynamic symbol table and `NEEDED` entries. High whole-file entropy and
a tiny section table suggest a packer (e.g. UPX leaves `UPX!` markers).
### Step 5: Skim strings and constructors
Check `.init_array`/constructors (code before `main`), and strings for paths, URLs, and shell
commands.
## Validation
- Header `type` and segment permissions are consistent with the inferred behavior.
- Imported symbols correspond to plausible capabilities.
- Packing call (entropy + missing sections) matches the strings observed.
## Pitfalls
- Assuming dynamic symbols are complete — they only cover imported/exported names, not
internal functions.
- Treating a stripped Go binary as "empty"; its structure lives in runtime metadata, not the
symbol table.
- Ignoring constructors/`.init_array`, which run before `main`.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the ELF analyzer.
- ELF/System V ABI spec and pyelftools (linked in frontmatter).

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# API Reference: Analyzing ELF Binaries on Linux
## Overview
`scripts/analyst.py` reports ELF header facts, dynamic symbols and `NEEDED` libraries, a strip
check, and capability groupings (network/process/crypto). It uses `pyelftools` when present
and falls back to a minimal header/entropy parse otherwise.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| pyelftools | >= 0.30 | Full ELF parsing (optional) |
| (stdlib) | — | Header/entropy fallback |
## External tools
| Tool | Purpose |
|------|---------|
| readelf / nm (binutils) | Authoritative ELF inspection |
| strings | Embedded string review |
## Core functions
### `analyze(path)`
Dispatches to the pyelftools or minimal parser; returns header, symbols, needed libs, and
capability groupings.
### `entropy(data)`
Byte-level Shannon entropy for packing assessment.
## Usage
```bash
pip install pyelftools # optional
python scripts/analyst.py analyze sample.elf
```
## Sources
- ELF / System V ABI specification — https://refspecs.linuxfoundation.org/elf/elf.pdf
- pyelftools — https://github.com/eliben/pyelftools
- UPX section/marker conventions (vendor docs)

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#!/usr/bin/env python3
"""Static ELF analysis: header, sections, dynamic symbols, and packing hints.
Uses ``pyelftools`` when available; otherwise parses the ELF header and section table
directly so basic facts (class, type, machine, sections, entropy) are still reported.
Never executes the sample.
"""
from __future__ import annotations
import argparse
import json
import math
import struct
import sys
from collections import Counter
from pathlib import Path
_NETWORK = {"socket", "connect", "send", "recv", "bind", "listen", "gethostbyname"}
_PROC = {"fork", "execve", "ptrace", "clone", "kill", "system", "popen"}
_CRYPTO = {"crypt", "evp_encryptinit_ex", "aes_encrypt", "rc4"}
def entropy(data: bytes) -> float:
if not data:
return 0.0
counts = Counter(data)
n = len(data)
return -sum((c / n) * math.log2(c / n) for c in counts.values())
def analyze_with_pyelftools(path: str) -> dict:
from elftools.elf.elffile import ELFFile # type: ignore
from elftools.elf.dynamic import DynamicSection # type: ignore
with open(path, "rb") as fh:
elf = ELFFile(fh)
header = {
"class": elf.elfclass,
"type": elf["e_type"],
"machine": elf["e_machine"],
"entry": hex(elf["e_entry"]),
}
symbols: list[str] = []
needed: list[str] = []
dynsym = elf.get_section_by_name(".dynsym")
if dynsym:
symbols = [s.name for s in dynsym.iter_symbols() if s.name]
for sec in elf.iter_sections():
if isinstance(sec, DynamicSection):
for tag in sec.iter_tags():
if tag.entry.d_tag == "DT_NEEDED":
needed.append(tag.needed)
stripped = elf.get_section_by_name(".symtab") is None
low = {s.lower() for s in symbols}
return {
"header": header,
"stripped": stripped,
"needed": needed,
"dynamic_symbol_count": len(symbols),
"capabilities": {
"network": sorted(low & _NETWORK),
"process": sorted(low & _PROC),
"crypto": sorted(low & _CRYPTO),
},
}
def analyze_minimal(path: str) -> dict:
data = Path(path).read_bytes()
if data[:4] != b"\x7fELF":
raise ValueError("not an ELF")
is64 = data[4] == 2
le = data[5] == 1
endian = "<" if le else ">"
e_type = struct.unpack_from(endian + "H", data, 16)[0]
e_machine = struct.unpack_from(endian + "H", data, 18)[0]
upx = b"UPX!" in data
return {
"header": {"class": 64 if is64 else 32, "type": e_type, "machine": e_machine},
"whole_file_entropy": round(entropy(data), 3),
"upx_marker": upx,
"note": "pyelftools not installed; minimal parse",
}
def analyze(path: str) -> dict:
try:
return analyze_with_pyelftools(path)
except ImportError:
return analyze_minimal(path)
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Static ELF analysis")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("analyze")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(analyze(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-excel-4-macro-malware
description: 'Analyzes legacy Excel 4.0 (XLM) macro malware by parsing extracted macro-sheet
formulas for auto-executing names, obfuscation (FORMULA.FILL, CHAR concatenation), and download
or execution primitives (EXEC, CALL, REGISTER). Activates for requests to analyze XLM macros,
examine Excel 4.0 macro sheets, or deobfuscate legacy spreadsheet macros.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- xlm
- excel4
- macros
- document-malware
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1059.005
- T1204.002
- T1027
d3fend:
- D3-FCR
- D3-DA
references:
- 'MITRE ATT&CK T1059.005 Visual Basic / macros — https://attack.mitre.org/techniques/T1059/005/'
- 'Microsoft Excel 4.0 macro functions reference — https://learn.microsoft.com/office/'
---
# Analyzing Excel 4.0 Macro Malware
## When to Use
- You have an extracted Excel 4.0 (XLM) macro sheet (e.g., from a `.xls`/`.xlsm` dumped with
oletools/XLMMacroDeobfuscator) and need to find auto-run cells and execution primitives.
- You are triaging maldocs that hide logic in legacy macro sheets rather than VBA.
**Do not use** Excel to open the document to "see" the macro — opening triggers the auto-run
cells. Work from the extracted formula text statically.
## Prerequisites
- The extracted XLM formula text (cell address → formula). Optional: `oletools` to extract it.
## Safety & Handling
- Never open the workbook in Excel; analyze the extracted formulas inertly. Defang URLs.
## Workflow
### Step 1: Find auto-executing entry points
```bash
python scripts/analyst.py analyze macros.txt
```
Flags defined-name triggers (`Auto_Open`, `Auto_Close`) and the cells they point to, plus
`=HALT()`/`=RETURN()` flow markers.
### Step 2: Identify execution and download primitives
Detects `EXEC(`, `CALL(`, `REGISTER(` (Win32 imports), and URLDownload-style `CALL` patterns that
fetch and run a payload.
### Step 3: Unwind obfuscation
Surfaces `CHAR()`/`&` string-building, `FORMULA.FILL`/`FORMULA` self-writing, and base/`MID`
slicing used to hide strings; reconstructs concatenated literals where possible.
### Step 4: Extract IOCs
Pull URLs/paths and defang them; record the execution method (regsvr32, rundll32, mshta).
## Validation
- Auto-run entry cells are identified, not just the presence of macros.
- Execution primitives (EXEC/CALL/REGISTER) are reported with their arguments.
- Reconstructed strings and URLs are defanged.
## Pitfalls
- Heavily obfuscated sheets that self-write cells at runtime — static reconstruction is partial.
- Macro sheets hidden as `Very Hidden` that simple viewers miss.
- Confusing benign legacy spreadsheets that legitimately use XLM.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the analyzer.
- ATT&CK T1059.005 and the Excel 4.0 macro reference (linked in frontmatter).

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# API Reference: Analyzing Excel 4.0 Macro Malware
## Overview
`scripts/analyst.py` parses an extracted XLM macro-sheet formula dump and flags auto-run triggers,
execution/download primitives, executors, and obfuscation, reconstructing `CHAR()` string chains
and defanging URLs. It parses text only, never opens the workbook, and executes nothing.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | regex, JSON, file IO |
| oletools | optional | extract the formulas upstream (not required by this script) |
## Core functions
### `analyze(path)`
Returns auto-run triggers, execution primitives, download indicators, executors, obfuscation
techniques, defanged URLs, reconstructed `CHAR()` strings, and a maliciousness flag.
### `_reconstruct_char(line)`
Rebuilds a string from a `CHAR(n)&CHAR(n)…` concatenation chain.
## Indicators
- Auto-run: `Auto_Open`, `Auto_Close`, `Auto_Activate`.
- Execution: `EXEC(`, `CALL(`, `REGISTER(` (Win32 import declaration).
- Download: `URLDownloadToFile`, `MSXML2`, `WinHTTP`.
## Usage
```bash
python scripts/analyst.py analyze macros.txt
```
## Sources
- MITRE ATT&CK T1059.005 — https://attack.mitre.org/techniques/T1059/005/
- Excel 4.0 macro function semantics (EXEC/CALL/REGISTER/FORMULA.FILL) from Microsoft Office docs
- XLM obfuscation patterns from public maldoc analysis (XLMMacroDeobfuscator methodology)

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#!/usr/bin/env python3
"""Analyze extracted Excel 4.0 (XLM) macro formulas for malicious behavior.
Reads a text dump of macro-sheet formulas and flags auto-run entry points, execution/download
primitives (EXEC/CALL/REGISTER), and obfuscation (CHAR concatenation, FORMULA.FILL self-writing).
Parses text only; executes nothing and never opens the workbook. Defangs recovered URLs.
"""
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
AUTO_NAMES = ["auto_open", "auto_close", "auto_activate", "auto_deactivate"]
EXEC_PRIMS = ["exec(", "call(", "register(", "fopen(", "fwrite("]
DOWNLOAD_HINTS = ["urlmon", "urldownloadtofile", "http", "msxml2", "winhttp", "shellexecute"]
EXECUTORS = ["regsvr32", "rundll32", "mshta", "powershell", "cmd.exe", "wscript", "cscript"]
OBFUSCATION = ["char(", "formula.fill", "formula(", "mid(", "&", "concatenate("]
RE_URL = re.compile(r"https?://[^\s\"'<>)]+", re.I)
RE_CHAR = re.compile(r"CHAR\((\d{1,3})\)", re.I)
def _defang(s: str) -> str:
return s.replace("http", "hxxp").replace(".", "[.]")
def _reconstruct_char(line: str) -> str:
chars = RE_CHAR.findall(line)
if len(chars) < 3:
return ""
try:
return "".join(chr(int(c)) for c in chars if 0 <= int(c) < 256)
except ValueError:
return ""
def analyze(path: str) -> dict:
text = Path(path).read_text(encoding="utf-8-sig", errors="replace")
low = text.lower()
auto = sorted({n for n in AUTO_NAMES if n in low})
exec_prims = sorted({p.rstrip("(") for p in EXEC_PRIMS if p in low})
downloads = sorted({d for d in DOWNLOAD_HINTS if d in low})
executors = sorted({e for e in EXECUTORS if e in low})
obf = sorted({o.rstrip("(") for o in OBFUSCATION if o in low})
urls = sorted({_defang(u) for u in RE_URL.findall(text)})
reconstructed = sorted({r for line in text.splitlines()
if (r := _reconstruct_char(line))})[:25]
return {
"auto_run_triggers": auto,
"execution_primitives": exec_prims,
"download_indicators": downloads,
"executors": executors,
"obfuscation_techniques": obf,
"urls": urls[:50],
"reconstructed_char_strings": reconstructed,
"is_likely_malicious_xlm": bool(auto and (exec_prims or downloads)),
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Analyze Excel 4.0 (XLM) macros")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("analyze")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(analyze(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-golang-malware-internals
description: 'Analyzes Go-compiled malware by recovering function names from the pclntab, detecting
the Go build/version string, and listing embedded package paths to overcome stripped symbols.
Activates for requests to analyze Go malware, recover Go function names, or identify a Golang
binary build.'
domain: cybersecurity
subdomain: reverse-engineering
tags:
- reverse-engineering
- golang
- symbol-recovery
- pclntab
- stripped-binaries
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1027
- T1587.001
- T1059
d3fend:
- D3-SDA
- D3-DA
references:
- 'Go runtime pclntab format (Go source: runtime/symtab.go) — https://go.dev/src/runtime/symtab.go'
- 'Go binary build info (debug/buildinfo) — https://pkg.go.dev/debug/buildinfo'
---
# Analyzing Golang Malware Internals
## When to Use
- You have a Go-compiled binary (often large, statically linked) and standard symbol recovery
fails because user symbols are stripped.
- You need function names from the pclntab, the Go version, and embedded package/module paths.
**Do not use** this for non-Go binaries — confirm the Go build signature first. This skill reads
the binary statically and executes nothing.
## Prerequisites
- The Go binary (read inertly).
## Safety & Handling
- Read bytes statically; treat strings as untrusted data.
## Workflow
### Step 1: Confirm it is Go and get the version
```bash
python scripts/analyst.py info sample.bin
```
Looks for the `Go build ID`, the `go1.x` version string, and `runtime.` references.
### Step 2: Recover function names from pclntab
The pclntab (preceded by a magic like `\xfb\xff\xff\xff`/`\xf0\xff\xff\xff` across Go versions)
contains function name strings; extract `main.*`, package-qualified, and `runtime.*` names.
### Step 3: Enumerate package paths
List embedded import/module paths (`github.com/...`, vendored deps) to fingerprint capabilities
and third-party libraries.
### Step 4: Map and document
Map suspicious packages (networking, crypto, exec) to behavior and ATT&CK.
## Validation
- The Go build signature/version is confirmed before deeper parsing.
- Recovered names include plausible `main.*`/package-qualified functions.
- Package paths are real module paths, not random strings.
## Pitfalls
- pclntab magic differs across Go versions; try the known set.
- Obfuscators (garble) rename symbols and strip build info — names may be hashed.
- Confusing vendored library code with the author's `main` package.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the analyzer.
- Go pclntab/buildinfo references (linked in frontmatter).

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# API Reference: Analyzing Golang Malware Internals
## Overview
`scripts/analyst.py` confirms a Go build (build ID / `go1.x` / `runtime.main`), detects pclntab
magic markers, and recovers Go function names and package import paths from printable strings,
mapping notable packages to capabilities. It reads bytes statically and executes nothing. This is
a heuristic scan, not a full pclntab structure parser.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | regex, JSON, file IO |
## Core functions
### `info(path)`
Returns Go detection, versions, pclntab magics present, recovered functions, package paths, and
capabilities.
## Heuristics
- Go detection: `Go build ID`, `go1.x`, `runtime.main`.
- pclntab magics: `\xfb\xff\xff\xff`, `\xfa…`, `\xf0…`, `\xf1…` (across Go versions).
- Functions: `main.*`, `runtime.*`, `net.*`, `crypto.*`, `os/exec.*`, `syscall.*`.
## Usage
```bash
python scripts/analyst.py info sample.bin
```
## Sources
- Go runtime pclntab format (runtime/symtab.go) — https://go.dev/src/runtime/symtab.go
- Go build info (debug/buildinfo) — https://pkg.go.dev/debug/buildinfo
- Go RE techniques (pclntab name recovery, garble obfuscation) from public Go-malware research

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#!/usr/bin/env python3
"""Analyze a Go-compiled binary: confirm Go build, recover names, list package paths.
Detects the Go build signature/version, scans printable strings for Go function/package names
(main.*, runtime.*, github.com/... import paths), and reports likely capabilities. Reads bytes
statically; executes nothing. This is a heuristic string/pclntab-marker scan, not a full pclntab
parser.
"""
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
ASCII_RE = re.compile(rb"[\x20-\x7e]{3,}")
RE_GOVER = re.compile(rb"go1\.\d+(?:\.\d+)?")
PCLNTAB_MAGICS = [b"\xfb\xff\xff\xff", b"\xfa\xff\xff\xff", b"\xf0\xff\xff\xff",
b"\xf1\xff\xff\xff"]
RE_FUNC = re.compile(r"^(?:main|runtime|net|crypto|os/exec|syscall)[./][\w./*()]+")
RE_PKG = re.compile(r"(?:github\.com|gitlab\.com|golang\.org|gopkg\.in)/[\w./-]+")
CAP_PKGS = {
"net/http": "HTTP networking", "net": "raw networking", "crypto/aes": "AES crypto",
"os/exec": "process execution", "syscall": "direct syscalls",
"golang.org/x/sys": "low-level OS access",
}
def info(path: str) -> dict:
data = Path(path).read_bytes()
strings = [m.decode("latin-1", "ignore") for m in ASCII_RE.findall(data)]
blob = "\n".join(strings)
is_go = b"Go build ID" in data or bool(RE_GOVER.search(data)) or b"runtime.main" in data
versions = sorted({m.decode() for m in RE_GOVER.findall(data)})
funcs = sorted({s for s in strings if RE_FUNC.match(s)})[:200]
pkgs = sorted(set(RE_PKG.findall(blob)))[:100]
caps = sorted({desc for pkg, desc in CAP_PKGS.items() if pkg in blob})
pcln = [m.hex() for m in PCLNTAB_MAGICS if m in data]
return {
"is_go_binary": is_go,
"go_versions": versions,
"pclntab_magics_present": pcln,
"recovered_functions": funcs,
"package_paths": pkgs,
"capabilities": caps,
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Analyze a Go binary")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("info")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(info(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-infostealer-credential-theft
description: 'Analyzes infostealer samples by mapping the browser, credential store, wallet, and
application paths they target, and the exfiltration channel used, from static strings and
behavior. Activates for requests to analyze an infostealer, identify targeted credential stores,
or map stealer collection and exfiltration behavior.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- infostealer
- credential-theft
- exfiltration
- triage
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1555.003
- T1539
- T1005
d3fend:
- D3-FAPA
- D3-DA
references:
- 'MITRE ATT&CK T1555.003 Credentials from Web Browsers — https://attack.mitre.org/techniques/T1555/003/'
- 'MITRE ATT&CK T1539 Steal Web Session Cookie — https://attack.mitre.org/techniques/T1539/'
---
# Analyzing Infostealer Credential Theft
## When to Use
- You have an infostealer sample and need to enumerate what it targets: browser credential/cookie
stores, crypto wallets, FTP/VPN/messaging app configs, and the exfil channel.
- You are building detections from the file paths and endpoints a stealer references.
**Do not use** this to recover credentials yourself — it characterizes what the malware targets
from inert static analysis.
## Prerequisites
- The sample (read inertly), optionally with extracted strings.
## Safety & Handling
- Read bytes statically; defang exfil endpoints; never run the stealer.
## Workflow
### Step 1: Map targeted artifacts
```bash
python scripts/analyst.py profile sample.bin
```
Matches references to known browser paths (`Login Data`, `Cookies`, `Web Data`), wallet
directories, app config paths, and credential APIs, grouped by category.
### Step 2: Identify exfiltration channel
Detects HTTP(S) POST endpoints, Telegram bot tokens, Discord webhooks, and FTP/SMTP usage in
strings.
### Step 3: Build the target/exfil profile
Summarize targeted stores and the exfil channel, mapping to ATT&CK.
### Step 4: Defang and report
Defang endpoints and produce IOCs for detection.
## Validation
- Targeted artifacts are grouped (browsers, wallets, apps, system credential stores).
- The exfil channel is identified with a defanged endpoint where present.
- Findings map to ATT&CK credential-access/collection/exfiltration techniques.
## Pitfalls
- Generic browser paths can appear in benign tools — corroborate with theft behavior.
- Missing wallet/app targets that use obfuscated path strings.
- Reporting live exfil endpoints (webhooks, bot tokens) without defanging.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the profiler.
- ATT&CK T1555.003 and T1539 (linked in frontmatter).

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# API Reference: Analyzing Infostealer Credential Theft
## Overview
`scripts/analyst.py` extracts strings from a sample and maps them to targeted artifacts (browser
credential stores, crypto wallets, application configs, credential APIs) and exfiltration channels
(Telegram, Discord webhooks, HTTP POST, FTP, SMTP). It reads bytes statically, defangs endpoints,
and executes nothing.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | regex, JSON, file IO |
## Core functions
### `profile(path)`
Returns targeted artifacts (grouped), exfiltration channels (defanged), and an infostealer
likelihood flag (≥2 target categories).
## Target categories
- **browser_credentials**: Chromium `Login Data`/`Cookies`/`Local State`, Firefox `key4.db`/`logins.json`.
- **crypto_wallets**: `wallet.dat`, Electrum/Exodus/MetaMask/Monero/Ledger.
- **applications**: FileZilla, OpenVPN, Telegram `tdata`, Discord, WinSCP, AnyDesk.
- **credential_api**: `CryptUnprotectData`, `CredEnumerate`, vault/LSA APIs.
## Usage
```bash
python scripts/analyst.py profile sample.bin
```
## Sources
- MITRE ATT&CK T1555.003 Credentials from Web Browsers — https://attack.mitre.org/techniques/T1555/003/
- MITRE ATT&CK T1539 Steal Web Session Cookie — https://attack.mitre.org/techniques/T1539/
- Browser/wallet artifact paths from public stealer family analyses

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#!/usr/bin/env python3
"""Profile an infostealer's targeted artifacts and exfiltration channel from static strings.
Extracts ASCII/UTF-16LE strings and matches them against curated lists of browser credential
stores, crypto wallets, application configs, and credential APIs, plus exfil-channel indicators
(HTTP POST, Telegram, Discord webhooks, FTP/SMTP). Reads bytes statically; executes nothing.
"""
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
ASCII_RE = re.compile(rb"[\x20-\x7e]{4,}")
UTF16_RE = re.compile(rb"(?:[\x20-\x7e]\x00){4,}")
TARGETS = {
"browser_credentials": ["login data", "web data", "cookies", "logins.json", "key4.db",
"signons.sqlite", "\\user data\\", "local state"],
"crypto_wallets": ["wallet.dat", "\\electrum\\", "\\exodus\\", "\\ethereum\\keystore",
"metamask", "\\monero\\", "ledger live"],
"applications": ["filezilla\\recentservers.xml", "\\openvpn\\", "\\telegram desktop\\tdata",
"\\discord\\local storage", "winscp.ini", "\\pidgin\\", "anydesk"],
"credential_api": ["cryptunprotectdata", "vaultcli", "credenumerate", "lsadump"],
}
EXFIL = {
"telegram": [r"api\.telegram\.org", r"bot\d{6,}:[a-z0-9_-]{30,}"],
"discord_webhook": [r"discord(?:app)?\.com/api/webhooks/"],
"http_post": [r"https?://[^\s\"']+"],
"ftp": [r"ftp://[^\s\"']+"],
"smtp": [r"smtp\.[a-z0-9.-]+"],
}
def _strings(data: bytes) -> str:
parts = [m.decode("ascii", "ignore") for m in ASCII_RE.findall(data)]
parts += [m.decode("utf-16-le", "ignore") for m in UTF16_RE.findall(data)]
return "\n".join(parts)
def _defang(s: str) -> str:
return s.replace("http", "hxxp").replace(".", "[.]")
def profile(path: str) -> dict:
blob = _strings(Path(path).read_bytes())
low = blob.lower()
targets = {}
for cat, needles in TARGETS.items():
hits = sorted({n for n in needles if n in low})
if hits:
targets[cat] = hits
exfil = {}
for chan, patterns in EXFIL.items():
found = set()
for pat in patterns:
for m in re.findall(pat, blob, re.I):
found.add(_defang(m))
if found:
exfil[chan] = sorted(found)[:10]
return {
"targeted_artifacts": targets,
"exfiltration_channels": exfil,
"is_likely_infostealer": len(targets) >= 2,
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Profile infostealer targets/exfil")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("profile")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(profile(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-java-jar-malware
description: 'Analyzes Java/JAR malware (such as Adwind/jRAT-class cross-platform RATs) by
inventorying the archive, reading the manifest entry point, detecting obfuscators and string
encryption, and flagging suspicious runtime, reflection, and networking class usage. Activates
for requests to analyze a malicious JAR, inspect Java malware, or identify a Java RAT.'
domain: cybersecurity
subdomain: reverse-engineering
tags:
- reverse-engineering
- java
- jar
- rat
- decompilation
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1059
- T1027
- T1620
d3fend:
- D3-SDA
- D3-DA
references:
- 'JAR/ZIP and Java class file format — https://docs.oracle.com/javase/specs/jvms/se17/html/'
- 'MITRE ATT&CK T1027 Obfuscated Files or Information — https://attack.mitre.org/techniques/T1027/'
---
# Analyzing Java/JAR Malware
## When to Use
- You have a malicious or suspicious `.jar` (often a cross-platform RAT) and need to map its
structure, entry point, obfuscation, and capability surface before decompiling.
- You want to triage a Java payload without running the JVM.
**Do not use** `java -jar` to run it — that executes the malware. Treat the JAR as a ZIP and read
its contents statically.
## Prerequisites
- The JAR file, read inertly. Optional: a Java decompiler (CFR, Procyon) for the next stage.
## Safety & Handling
- Read the archive statically; never launch the JVM on the sample. Defang any URLs found.
## Workflow
### Step 1: Inventory the archive and entry point
```bash
python scripts/analyst.py inspect sample.jar
```
Lists `.class` files, embedded resources/payloads (nested JARs, scripts, encrypted blobs), and
reads `META-INF/MANIFEST.MF` for `Main-Class`/`Premain-Class`.
### Step 2: Detect obfuscation and packers
Flags obfuscator fingerprints (Allatori, ProGuard, Zelix), single-character class/package names,
and string-decryption indicators.
### Step 3: Flag capability classes
Surface dangerous API usage in strings/constant pools: `Runtime.exec`/`ProcessBuilder`,
`java.lang.reflect`, `URLClassLoader`, `javax.crypto`, `java.net.Socket`, registry/persistence
helpers.
### Step 4: Route to decompilation
Hand the key classes to a decompiler (CFR/Procyon) for source recovery; record IOCs.
## Validation
- The manifest entry point is read and reported.
- Embedded payloads/nested archives are enumerated.
- Capability flags are backed by concrete class/string evidence.
## Pitfalls
- String-encrypted samples where capability strings appear only after decryption.
- Multi-stage droppers that unpack a second JAR at runtime.
- Benign obfuscated commercial JARs — corroborate with capability and delivery context.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the inspector.
- JVM/JAR format and ATT&CK T1027 references (linked in frontmatter).

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# API Reference: Analyzing Java/JAR Malware
## Overview
`scripts/analyst.py` treats a JAR as a ZIP and inventories `.class` files and embedded payloads,
reads the manifest entry point, detects obfuscator fingerprints and name obfuscation, and scans
class constant pools for dangerous API usage. It reads the archive statically, never launches the
JVM, and defangs URLs.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | zipfile, regex, JSON, file IO |
| CFR / Procyon | optional | decompile recovered classes downstream (not required here) |
## Core functions
### `inspect(path)`
Returns class count, `Main-Class`, embedded payloads, resources, obfuscators, a name-obfuscation
flag, capability APIs, and defanged URLs.
## Capability APIs flagged
`Runtime`/`ProcessBuilder` (exec), `java.lang.reflect` + `URLClassLoader` (reflective loading),
`javax.crypto` (encryption), `java.net.Socket`/`URL` (networking), `java.awt.Robot` (surveillance).
## Usage
```bash
python scripts/analyst.py inspect sample.jar
```
## Sources
- JVM specification (class file format, constant pool) — https://docs.oracle.com/javase/specs/jvms/se17/html/
- JAR/ZIP structure and `MANIFEST.MF` `Main-Class` from the Java platform documentation
- Obfuscator fingerprints (Allatori/ProGuard/Zelix) from public Java-malware analyses

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#!/usr/bin/env python3
"""Inspect a Java/JAR sample statically: structure, manifest, obfuscation, capabilities.
Treats the JAR as a ZIP, inventories .class files and embedded payloads, reads the manifest entry
point, and scans class bytes for obfuscator fingerprints and dangerous API usage. Reads the archive
statically; never launches the JVM. Defangs URLs.
"""
from __future__ import annotations
import argparse
import json
import re
import sys
import zipfile
from pathlib import Path
EMBED_SIGS = [(b"PK\x03\x04", "nested ZIP/JAR"), (b"MZ", "PE executable"),
(b"#!", "script"), (b"\x7fELF", "ELF")]
OBFUSCATORS = [b"allatori", b"proguard", b"zelix", b"klassmaster", b"stringer", b"dasho"]
CAPABILITY_APIS = [b"java/lang/Runtime", b"java/lang/ProcessBuilder", b"java/lang/reflect",
b"java/net/URLClassLoader", b"javax/crypto", b"java/net/Socket",
b"java/net/URL", b"java/awt/Robot", b"java/util/Base64"]
RE_URL = re.compile(rb"https?://[\x21-\x7e]{4,}")
def _defang(s: str) -> str:
return s.replace("http", "hxxp").replace(".", "[.]")
def inspect(path: str) -> dict:
data = Path(path).read_bytes()
result: dict = {"is_jar": False}
if not zipfile.is_zipfile(path):
return {"error": "not a ZIP/JAR archive"}
result["is_jar"] = True
classes = []
resources = []
manifest = ""
obf = set()
caps = set()
urls = set()
nested = []
with zipfile.ZipFile(path) as zf:
for name in zf.namelist():
if name.endswith(".class"):
classes.append(name)
try:
body = zf.read(name)
except Exception:
continue
for o in OBFUSCATORS:
if o in body.lower():
obf.add(o.decode())
for api in CAPABILITY_APIS:
if api in body:
caps.add(api.decode().replace("/", "."))
for u in RE_URL.findall(body):
urls.add(_defang(u.decode("latin-1", "ignore")))
elif name == "META-INF/MANIFEST.MF":
manifest = zf.read(name).decode("latin-1", "ignore")
else:
resources.append(name)
try:
head = zf.read(name)[:8]
except Exception:
head = b""
for sig, label in EMBED_SIGS:
if head.startswith(sig):
nested.append({"name": name, "type": label})
main_class = ""
for line in manifest.splitlines():
if line.lower().startswith(("main-class:", "premain-class:")):
main_class = line.split(":", 1)[1].strip()
break
# single-character class names suggest name obfuscation
short_names = sum(1 for c in classes if len(Path(c).stem) <= 2)
return {
"is_jar": True,
"class_count": len(classes),
"main_class": main_class,
"embedded_payloads": nested[:50],
"resources": resources[:50],
"obfuscators": sorted(obf),
"name_obfuscation_suspected": short_names > max(3, len(classes) // 5),
"capability_apis": sorted(caps),
"urls": sorted(urls)[:50],
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Inspect a Java/JAR sample")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("inspect")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(inspect(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-loaders-and-droppers
description: 'Analyzes loader and dropper samples by identifying staging behavior — embedded or
downloaded next-stage payloads, decode/decrypt routines, and the execution mechanism used to run
the next stage. Activates for requests to analyze a loader or dropper, find a staged payload, or
map multi-stage delivery behavior.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- loader
- dropper
- multi-stage
- delivery
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1105
- T1140
- T1574
d3fend:
- D3-DA
- D3-FCR
references:
- 'MITRE ATT&CK T1105 Ingress Tool Transfer — https://attack.mitre.org/techniques/T1105/'
- 'MITRE ATT&CK T1140 Deobfuscate/Decode Files or Information — https://attack.mitre.org/techniques/T1140/'
---
# Analyzing Loaders and Droppers
## When to Use
- You have a first-stage loader/dropper and need to find the next stage: an embedded resource, an
appended overlay, or a download URL.
- You need to identify the decode/decrypt routine and the execution mechanism (process creation,
injection, scheduled task, DLL side-loading).
**Do not use** this to run the loader and fetch live stages — characterize staging statically and
retrieve next stages only in an isolated sandbox.
## Prerequisites
- The loader/dropper sample (read inertly).
## Safety & Handling
- Read bytes statically; defang download URLs; never execute to pull live payloads.
## Workflow
### Step 1: Locate staged payloads
```bash
python scripts/analyst.py stage sample.bin
```
Detects embedded PE/archive signatures, an appended overlay beyond the PE's mapped size, large
high-entropy blobs, and download URLs.
### Step 2: Identify decode/execution mechanism
Scan imports/strings for decode APIs (`CryptDecrypt`, base64), download APIs (`URLDownloadToFile`,
`WinHttp`, `InternetReadFile`), and execution (`CreateProcess`, `ShellExecute`, `WinExec`,
side-loading hints).
### Step 3: Map the delivery chain
Document stage-1 → decode → stage-2 → execution and map each step to ATT&CK.
### Step 4: Defang and report
Defang URLs and produce IOCs for the staging infrastructure.
## Validation
- Embedded/overlay stages are confirmed by signature or entropy, with offsets recorded.
- Download URLs and execution mechanism are identified from imports/strings.
- The multi-stage chain maps cleanly to ATT&CK techniques.
## Pitfalls
- Treating a benign overlay (installer data, signature) as a payload without corroboration.
- Missing stages fetched at runtime when no URL is in plaintext (obfuscated/encoded).
- Confusing the decode routine for the payload itself.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the stager.
- ATT&CK T1105 and T1140 (linked in frontmatter).

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# API Reference: Analyzing Loaders and Droppers
## Overview
`scripts/analyst.py` analyzes a loader/dropper: it finds embedded PE/archive signatures (skipping
the loader's own header), counts high-entropy windows (packed/encrypted stages), and scans strings
for download, decode, and execution APIs plus URLs. It reads bytes statically, defangs URLs, and
executes nothing.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | math, regex, Counter, JSON, file IO |
## Core functions
### `stage(path)`
Returns embedded signatures, high-entropy window count, download/decode/execution API hits,
defanged URLs, and a loader likelihood flag.
### `_entropy(chunk)`
Shannon entropy (bits/byte) over a window; >7.2 suggests compressed/encrypted data.
## API categories
- **download**: `URLDownloadToFile`, `InternetReadFile`, WinHTTP/WinINet.
- **decode**: `CryptDecrypt`, `CryptStringToBinary`, base64/RC4/XOR.
- **execution**: `CreateProcess`, `ShellExecute`, `WinExec`, `CreateRemoteThread`, `LoadLibrary`.
## Usage
```bash
python scripts/analyst.py stage sample.bin
```
## Sources
- MITRE ATT&CK T1105 Ingress Tool Transfer — https://attack.mitre.org/techniques/T1105/
- MITRE ATT&CK T1140 Deobfuscate/Decode Information — https://attack.mitre.org/techniques/T1140/
- Shannon entropy definition; overlay/staging patterns from public loader analyses

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#!/usr/bin/env python3
"""Analyze a loader/dropper: locate staged payloads and identify decode/download/execution APIs.
Detects embedded PE/archive signatures, scans strings for download/decode/execution APIs and
URLs, and computes Shannon entropy over windows to flag large high-entropy (packed/encrypted)
blobs. Reads bytes statically; executes nothing.
"""
from __future__ import annotations
import argparse
import json
import math
import re
import sys
from collections import Counter
from pathlib import Path
ASCII_RE = re.compile(rb"[\x20-\x7e]{4,}")
UTF16_RE = re.compile(rb"(?:[\x20-\x7e]\x00){4,}")
RE_URL = re.compile(r"https?://[^\s\"'<>]+", re.I)
EMBED_SIGS = [(b"MZ", "PE"), (b"PK\x03\x04", "ZIP/Office"), (b"Rar!\x1a\x07", "RAR"),
(b"\x1f\x8b", "GZIP"), (b"7z\xbc\xaf\x27\x1c", "7z")]
DOWNLOAD_API = ["urldownloadtofile", "internetreadfile", "internetopenurl", "winhttp",
"httpopenrequest", "wininet"]
DECODE_API = ["cryptdecrypt", "cryptstringtobinary", "base64", "rc4", "xor"]
EXEC_API = ["createprocess", "shellexecute", "winexec", "createremotethread", "loadlibrary",
"ntcreateuserprocess", "wow64"]
def _strings(data: bytes) -> str:
parts = [m.decode("ascii", "ignore") for m in ASCII_RE.findall(data)]
parts += [m.decode("utf-16-le", "ignore") for m in UTF16_RE.findall(data)]
return "\n".join(parts)
def _entropy(chunk: bytes) -> float:
if not chunk:
return 0.0
counts = Counter(chunk)
total = len(chunk)
return -sum((c / total) * math.log2(c / total) for c in counts.values())
def stage(path: str) -> dict:
data = Path(path).read_bytes()
embedded = []
for sig, label in EMBED_SIGS:
idx = data.find(sig, 1) # skip offset 0 (the loader's own header)
if idx != -1:
embedded.append({"offset": idx, "type": label})
win = 4096
high_entropy = sum(1 for i in range(0, len(data), win)
if _entropy(data[i:i + win]) > 7.2)
blob = _strings(data)
low = blob.lower()
urls = sorted({u.replace("http", "hxxp").replace(".", "[.]") for u in RE_URL.findall(blob)})
return {
"embedded_signatures": embedded,
"high_entropy_windows": high_entropy,
"download_apis": sorted({a for a in DOWNLOAD_API if a in low}),
"decode_apis": sorted({a for a in DECODE_API if a in low}),
"execution_apis": sorted({a for a in EXEC_API if a in low}),
"urls": urls[:50],
"is_likely_loader": bool(embedded) or bool(urls and any(a in low for a in DOWNLOAD_API)),
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Analyze loader/dropper staging")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("stage")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(stage(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-mach-o-binaries-on-macos
description: 'Statically analyzes macOS Mach-O malware: parsing the header and load commands,
handling fat/universal binaries, reading linked dylibs and entitlements, and checking code
signatures to infer capability and trust. Activates for requests to analyze a Mach-O binary,
inspect macOS malware, or parse load commands and entitlements.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- mach-o
- macos
- static-analysis
- code-signing
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1027
- T1059.002
d3fend:
- D3-FCR
- D3-DA
references:
- 'Apple Mach-O file format reference — https://github.com/apple-oss-distributions/xnu/blob/main/EXTERNAL_HEADERS/mach-o/loader.h'
- 'macOS code signing overview — https://developer.apple.com/documentation/security/code_signing_services'
---
# Analyzing Mach-O Binaries on macOS
## When to Use
- You have a macOS sample (Mach-O) and need a static capability and trust read.
- You must handle a fat/universal binary containing multiple architecture slices.
- You need to inspect linked dylibs, entitlements, and code-signing status.
**Do not use** Windows PE tooling on Mach-O — the formats differ entirely; use Mach-O-aware
parsers.
## Prerequisites
- A Mach-O parser (Python stdlib `struct`, or `macholib`/LIEF); the sample handled inertly.
- For signing/entitlements on macOS, `codesign`/`otool` are authoritative.
## Safety & Handling
- Parse statically; never execute the sample, especially on a real macOS host.
- Keep the sample password-protected at rest and reference it by hash.
## Workflow
### Step 1: Detect fat vs. thin and architecture
Check the magic: `0xCAFEBABE` (fat/universal) vs. `0xFEEDFACE`/`0xFEEDFACF` (Mach-O 32/64). For
fat binaries, enumerate and analyze each slice.
```bash
python scripts/analyst.py header sample.macho
```
### Step 2: Parse load commands
Read load commands for linked dylibs (`LC_LOAD_DYLIB`), entry point (`LC_MAIN`), and signing
(`LC_CODE_SIGNATURE`). The dylib list hints at capability (networking, crypto).
### Step 3: Inspect entitlements and signing
On macOS, use `codesign`/`otool` to read entitlements and verify the signature. Ad-hoc or absent
signatures and suspicious entitlements are risk indicators.
### Step 4: Infer capability and route
Map linked frameworks/symbols to behaviors and route to disassembly/RE for deeper analysis.
## Validation
- Fat binaries are decomposed and each slice is analyzed, not just the first.
- Load commands, dylibs, and signing status are enumerated correctly.
- Capability inferences are corroborated by linked frameworks/symbols.
## Pitfalls
- Analyzing only one slice of a universal binary.
- Trusting a present signature without verifying it (ad-hoc signatures verify but aren't trusted).
- Applying PE assumptions (sections/imports) to Mach-O structures.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the Mach-O header parser.
- Apple Mach-O loader.h and code signing docs (linked in frontmatter).

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# API Reference: Analyzing Mach-O Binaries on macOS
## Overview
`scripts/analyst.py` parses Mach-O headers with stdlib `struct`: it detects fat/universal vs. thin
binaries, enumerates slices, and reads load commands for linked dylibs, the entry point, and the
presence of a code signature. It parses statically and never executes the sample.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | struct parsing, file IO |
## External tools
| Tool | Purpose |
|------|---------|
| codesign | Verify signature and read entitlements on macOS |
| otool | Inspect load commands and linked libraries |
## Core functions
### `parse(path)`
Dispatches on magic to handle fat vs. thin binaries; returns format, slices, and per-slice data.
### `parse_thin(data, base)`
Parses one Mach-O image's load commands (dylibs, LC_MAIN, LC_CODE_SIGNATURE).
## Usage
```bash
python scripts/analyst.py header sample.macho
```
## Sources
- Apple Mach-O `loader.h` (header and load-command structures) —
https://github.com/apple-oss-distributions/xnu/blob/main/EXTERNAL_HEADERS/mach-o/loader.h
- macOS code signing overview —
https://developer.apple.com/documentation/security/code_signing_services
- Fat/universal binary layout (FAT_MAGIC + slice table), Mach-O format documentation

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#!/usr/bin/env python3
"""Parse a Mach-O (or fat/universal) binary header and key load commands.
Detects fat vs. thin Mach-O, enumerates architecture slices, and parses load commands for linked
dylibs, the main entry point, and the presence of a code signature. Uses stdlib ``struct`` only.
It parses bytes statically and never executes the sample.
"""
from __future__ import annotations
import argparse
import json
import struct
import sys
from pathlib import Path
MH_MAGIC = 0xFEEDFACE
MH_MAGIC_64 = 0xFEEDFACF
MH_CIGAM = 0xCEFAEDFE
MH_CIGAM_64 = 0xCFFAEDFE
FAT_MAGIC = 0xCAFEBABE
FAT_CIGAM = 0xBEBAFECF
LC_LOAD_DYLIB = 0x0C
LC_MAIN = 0x80000028
LC_CODE_SIGNATURE = 0x1D
def _u32(data: bytes, off: int, be: bool) -> int:
return struct.unpack_from(">I" if be else "<I", data, off)[0]
def parse_thin(data: bytes, base: int) -> dict:
magic = struct.unpack_from(">I", data, base)[0]
be = magic in (MH_MAGIC, MH_MAGIC_64)
is64 = magic in (MH_MAGIC_64, MH_CIGAM_64)
hdr = base
ncmds = _u32(data, hdr + 16, be)
off = hdr + (32 if is64 else 28)
dylibs, signed, entry = [], False, None
for _ in range(ncmds):
cmd = _u32(data, off, be)
cmdsize = _u32(data, off + 4, be)
if cmdsize == 0:
break
if cmd == LC_LOAD_DYLIB:
name_off = _u32(data, off + 8, be)
raw = data[off + name_off:off + cmdsize].split(b"\x00", 1)[0]
dylibs.append(raw.decode("latin-1"))
elif cmd == (LC_MAIN & 0xFFFFFFFF) or cmd == LC_MAIN:
entry = "LC_MAIN"
elif cmd == LC_CODE_SIGNATURE:
signed = True
off += cmdsize
return {"arch": "64-bit" if is64 else "32-bit", "dylibs": dylibs,
"has_code_signature": signed, "entry": entry}
def parse(path: str) -> dict:
data = Path(path).read_bytes()
magic = struct.unpack_from(">I", data, 0)[0]
if magic in (FAT_MAGIC, FAT_CIGAM):
nfat = struct.unpack_from(">I", data, 4)[0]
slices = []
for i in range(nfat):
entry_off = 8 + i * 20
offset = struct.unpack_from(">I", data, entry_off + 8)[0]
slices.append(parse_thin(data, offset))
return {"format": "fat/universal", "slice_count": nfat, "slices": slices}
if magic in (MH_MAGIC, MH_MAGIC_64, MH_CIGAM, MH_CIGAM_64):
return {"format": "thin Mach-O", **parse_thin(data, 0)}
return {"error": "not a Mach-O binary"}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Parse Mach-O header/load commands")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("header")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(parse(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-malicious-iso-and-container-files
description: 'Analyzes malicious ISO, IMG, VHD, and similar container files used to smuggle payloads
past email and mark-of-the-web defenses by enumerating contents and flagging hidden executables,
LNKs, and scripts. Activates for requests to analyze a malicious ISO/IMG, inspect a container
delivery file, or find payloads hidden inside a disk image attachment.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- iso
- container
- delivery
- mark-of-the-web
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1553.005
- T1204.002
- T1027
d3fend:
- D3-FCR
- D3-FA
references:
- 'MITRE ATT&CK T1553.005 Mark-of-the-Web Bypass — https://attack.mitre.org/techniques/T1553/005/'
- 'ISO 9660 file system — https://wiki.osdev.org/ISO_9660'
---
# Analyzing Malicious ISO and Container Files
## When to Use
- You have an ISO/IMG/VHD attachment suspected of smuggling a payload (LNK + hidden DLL/EXE/script)
past MOTW and scanners.
- You need to enumerate contents and flag hidden/executable entries without mounting the image.
**Do not use** mounting/auto-run to inspect — that can trigger the lure. Parse the container's
directory records statically.
## Prerequisites
- The container file (read inertly). Optional: `pycdlib` for full ISO parsing (the script falls
back to signature/string carving if absent).
## Safety & Handling
- Never mount or open the container interactively; carve statically. Defang any URLs found.
## Workflow
### Step 1: Identify the container type
```bash
python scripts/analyst.py inspect sample.iso
```
Detects ISO 9660 (`CD001`), UDF, FAT/VHD signatures and reports the format.
### Step 2: Enumerate entries and flag payloads
Lists embedded file signatures (`MZ`, `PK`, LNK `\x4C\x00\x00\x00`, script headers) and flags
hidden/executable/decoy entries.
### Step 3: Assess the lure chain
A typical chain is a visible LNK pointing at a hidden DLL/EXE/script in the same image — document
the relationship.
### Step 4: Extract and route
Carve the hidden payload and route it to the appropriate analysis workflow; defang URLs.
## Validation
- The container format is identified by signature.
- Hidden executables/LNKs/scripts are enumerated with offsets.
- The lure→payload relationship is documented.
## Pitfalls
- Mounting the image and triggering the lure.
- Missing payloads in less-common container formats (VHDX, UDF) without the right parser.
- Overlooking hidden-attribute files that the file manager would not show.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the inspector.
- ATT&CK T1553.005 and ISO 9660 references (linked in frontmatter).

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# API Reference: Analyzing Malicious ISO and Container Files
## Overview
`scripts/analyst.py` identifies a container's format (ISO 9660, VHD/VHDX, FAT/IMG) by signature and
carves embedded file signatures (PE, ZIP/Office, LNK, scripts) with offsets. With pycdlib it adds a
root directory listing. It reads bytes statically and never mounts the image.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| pycdlib | latest | full ISO directory listing (optional) |
| (stdlib) | — | JSON, file IO |
## Core functions
### `inspect(path)`
Returns format, ISO directory (if pycdlib), embedded signatures, and a hidden-payload flag.
### `_format(data)`
Signature-based container format detection (`CD001`, `conectix`, `vhdxfile`, FAT markers).
## Carved signatures
PE (`MZ`), ZIP/Office (`PK`), LNK header, shebang/WSF/batch scripts.
## Usage
```bash
python scripts/analyst.py inspect sample.iso
```
## Sources
- MITRE ATT&CK T1553.005 Mark-of-the-Web Bypass — https://attack.mitre.org/techniques/T1553/005/
- ISO 9660 (`CD001` at sector 16) — https://wiki.osdev.org/ISO_9660
- VHD `conectix` / VHDX `vhdxfile` signatures from Microsoft VHD/VHDX specifications

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#!/usr/bin/env python3
"""Inspect ISO/IMG/VHD container files for hidden/smuggled payloads.
Identifies the container format by signature and carves embedded file signatures (PE, archives,
LNK, scripts) with offsets. Uses pycdlib for full ISO directory listing when available; otherwise
falls back to signature carving. Reads bytes statically; never mounts the image.
"""
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
try:
import pycdlib # type: ignore
HAVE_PYCDLIB = True
except ImportError:
HAVE_PYCDLIB = False
EMBED_SIGS = [
(b"MZ", "PE executable"),
(b"PK\x03\x04", "ZIP/Office"),
(b"\x4c\x00\x00\x00\x01\x14\x02\x00", "LNK shortcut"),
(b"#!", "script (shebang)"),
(b"<job", "WSF job"),
(b"@echo", "batch script"),
]
def _format(data: bytes) -> str:
if data[0x8001:0x8006] == b"CD001" or b"CD001" in data[:0x10000]:
return "ISO 9660"
if data[:8] == b"conectix":
return "VHD"
if data[:8] == b"vhdxfile":
return "VHDX"
if data[0x36:0x3b] == b"FAT16" or data[0x52:0x57] == b"FAT32":
return "FAT (IMG)"
return "unknown"
def _carve(data: bytes) -> list[dict]:
found = []
for sig, label in EMBED_SIGS:
start = 0
while len(found) < 100:
idx = data.find(sig, start)
if idx == -1:
break
found.append({"offset": idx, "type": label})
start = idx + 1
return found
def _iso_listing(path: str) -> list[str]:
if not HAVE_PYCDLIB:
return []
names = []
try:
iso = pycdlib.PyCdlib()
iso.open(path)
for child in iso.list_children(iso_path="/"):
ident = getattr(child.file_identifier(), "decode", lambda *_: "")("utf-8", "ignore")
if ident and ident not in (".", ".."):
names.append(ident)
iso.close()
except Exception:
return []
return names
def inspect(path: str) -> dict:
data = Path(path).read_bytes()
carved = _carve(data)
return {
"format": _format(data),
"pycdlib_available": HAVE_PYCDLIB,
"iso_directory": _iso_listing(path),
"embedded_signatures": carved,
"hidden_payload_suspected": any(c["type"].startswith(("PE", "LNK", "script"))
for c in carved),
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Inspect ISO/container files")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("inspect")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(inspect(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-malicious-lnk-files
description: 'Analyzes weaponized Windows shortcut (.lnk) files: parsing the shell link structure
for the target command, arguments, icon, and working directory, and recovering hidden
PowerShell/cmd payloads and embedded content used in phishing. Activates for requests to
analyze a malicious LNK, parse a shortcut file, or extract a command from a .lnk.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- lnk
- shortcut
- document-malware
- static-analysis
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1204.002
- T1059.001
- T1059.003
d3fend:
- D3-FCR
- D3-DA
references:
- 'Microsoft [MS-SHLLINK] Shell Link Binary File Format — https://learn.microsoft.com/openspecs/windows_protocols/ms-shllink/'
- 'LNK forensic structure references — https://github.com/libyal/liblnk/blob/main/documentation/Windows%20Shortcut%20File%20(LNK)%20format.asciidoc'
---
# Analyzing Malicious LNK Files
## When to Use
- You have a `.lnk` delivered via phishing (often in an archive or ISO) and need its real action.
- You must recover the target command line, arguments, and any embedded/hidden payload.
- You are extracting IOCs (commands, URLs, file paths) from a shortcut.
**Do not use** Windows Explorer to inspect or double-click the shortcut — that executes its
target. Parse the binary structure statically.
## Prerequisites
- An LNK parser (Python `struct`, or `lnkparse`/`LECmd`); the sample handled inertly.
## Safety & Handling
- Never double-click or hover-load the shortcut in Explorer; parse raw bytes only.
- Defang recovered URLs and store any embedded payload password-protected.
## Workflow
### Step 1: Confirm and parse the header
Verify the LNK magic (`4C 00 00 00` + the link CLSID) and read `LinkFlags` to know which optional
structures are present.
```bash
python scripts/analyst.py parse sample.lnk
```
### Step 2: Recover the target and arguments
Extract the relative/local path and, crucially, the `COMMAND_LINE_ARGUMENTS` string — malicious
LNKs hide `powershell -enc ...` or `cmd /c ...` here.
### Step 3: Inspect icon and metadata
Read the icon location (often spoofed to look like a document) and metadata (machine ID,
timestamps) useful for attribution/clustering.
### Step 4: Find appended/embedded payloads
Check for data appended after the LNK structure (some campaigns embed scripts/binaries past the
parsed end) and extract it.
### Step 5: Decode and extract IOCs
Decode encoded commands (UTF-16LE base64), defang URLs, and route extracted payloads to the right
analysis workflow.
## Validation
- The full command line, including arguments, is recovered (not just the target path).
- Encoded commands are decoded to cleartext.
- Appended/embedded payloads are detected and extracted, and IOCs are defanged.
## Pitfalls
- Reading only the target path and missing the malicious arguments.
- Inspecting in Explorer and executing the shortcut.
- Ignoring data appended past the LNK structure.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the LNK parser.
- MS-SHLLINK and the liblnk format notes (linked in frontmatter).

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# API Reference: Analyzing Malicious LNK Files
## Overview
`scripts/analyst.py` is a minimal [MS-SHLLINK] parser: it validates the LNK header and CLSID, reads
LinkFlags, skips the IDList/LinkInfo, and extracts the StringData fields (name, relative path,
working dir, arguments, icon), plus the count of any appended data. It parses statically and never
executes the shortcut.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | struct parsing, file IO |
## External tools
| Tool | Purpose |
|------|---------|
| LECmd / lnkparse | Full-featured LNK parsing including extra data blocks |
## Core functions
### `parse(path)`
Returns LinkFlags and the recovered StringData fields, appended-data size, and a suspicious flag.
### `_read_stringdata(data, off, unicode_)`
Reads a CountedString (ANSI or UTF-16LE) per the IsUnicode flag.
## Usage
```bash
python scripts/analyst.py parse sample.lnk
```
## Sources
- Microsoft [MS-SHLLINK] Shell Link Binary File Format —
https://learn.microsoft.com/openspecs/windows_protocols/ms-shllink/
- liblnk LNK format documentation —
https://github.com/libyal/liblnk/blob/main/documentation/Windows%20Shortcut%20File%20(LNK)%20format.asciidoc
- Malicious-argument hiding in COMMAND_LINE_ARGUMENTS, established document-analysis practice

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#!/usr/bin/env python3
"""Parse a Windows shell link (.lnk) and recover its command and arguments.
Implements a minimal [MS-SHLLINK] parser using stdlib ``struct``: it validates the header,
reads LinkFlags, and walks the optional StringData section to extract the name, relative path,
working directory, command-line arguments, and icon location. It also reports any data appended
past the structure. It parses bytes statically and never executes the shortcut.
"""
from __future__ import annotations
import argparse
import json
import struct
import sys
from pathlib import Path
HEADER_SIZE = 0x4C
LINK_CLSID = b"\x01\x14\x02\x00\x00\x00\x00\x00\xc0\x00\x00\x00\x00\x00\x00\x46"
# LinkFlags bits relevant to StringData (MS-SHLLINK 2.1.1)
F_HAS_LINK_TARGET_IDLIST = 0x00000001
F_HAS_NAME = 0x00000004
F_HAS_RELATIVE_PATH = 0x00000008
F_HAS_WORKING_DIR = 0x00000010
F_HAS_ARGUMENTS = 0x00000020
F_HAS_ICON_LOCATION = 0x00000040
F_IS_UNICODE = 0x00000080
def _read_stringdata(data: bytes, off: int, unicode_: bool) -> tuple[str, int]:
count = struct.unpack_from("<H", data, off)[0]
off += 2
if unicode_:
raw = data[off:off + count * 2]
off += count * 2
return raw.decode("utf-16-le", "replace"), off
raw = data[off:off + count]
off += count
return raw.decode("latin-1", "replace"), off
def parse(path: str) -> dict:
data = Path(path).read_bytes()
if len(data) < HEADER_SIZE or struct.unpack_from("<I", data, 0)[0] != HEADER_SIZE:
return {"error": "not a LNK (bad HeaderSize)"}
if data[4:20] != LINK_CLSID:
return {"error": "not a LNK (bad CLSID)"}
flags = struct.unpack_from("<I", data, 20)[0]
unicode_ = bool(flags & F_IS_UNICODE)
off = HEADER_SIZE
# Skip LinkTargetIDList if present
if flags & F_HAS_LINK_TARGET_IDLIST:
idlist_size = struct.unpack_from("<H", data, off)[0]
off += 2 + idlist_size
# Skip LinkInfo if present (HasLinkInfo = 0x2)
if flags & 0x00000002:
link_info_size = struct.unpack_from("<I", data, off)[0]
off += link_info_size
result = {"flags": hex(flags), "unicode": unicode_}
for bit, key in [(F_HAS_NAME, "name"), (F_HAS_RELATIVE_PATH, "relative_path"),
(F_HAS_WORKING_DIR, "working_dir"), (F_HAS_ARGUMENTS, "arguments"),
(F_HAS_ICON_LOCATION, "icon_location")]:
if flags & bit and off < len(data):
try:
value, off = _read_stringdata(data, off, unicode_)
result[key] = value
except struct.error:
break
result["appended_data_bytes"] = max(0, len(data) - off)
args = result.get("arguments", "")
result["suspicious"] = any(t in args.lower() for t in
("powershell", "-enc", "cmd", "mshta", "rundll32", "frombase64"))
return result
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Parse a malicious LNK")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("parse")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(parse(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-malicious-office-macros
description: 'Analyzes malicious Office documents by extracting and reviewing VBA macros and
OLE/OOXML structure: auto-exec triggers, obfuscation, dropped payloads, and shell/PowerShell
invocation. Activates for requests to analyze a malicious Word/Excel document, VBA macro, or
maldoc.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware
- maldoc
- vba
- office
- olevba
- phishing
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1566.001
- T1059.005
- T1204.002
d3fend:
- D3-FCR
- D3-DA
references:
- 'oletools (olevba) — https://github.com/decalage2/oletools'
- 'MS-OVBA VBA file format — https://learn.microsoft.com/openspecs/office_file_formats/ms-ovba/'
---
# Analyzing Malicious Office Macros
## When to Use
- A Word/Excel/PowerPoint document is suspected of delivering malware via macros.
- You need to extract VBA, identify auto-exec triggers, and see what the macro does.
- You are determining the next-stage payload or URL a maldoc fetches.
**Do not use** the document's "Enable Content" path to analyze it — never open a maldoc in
Office. Extract and read the macro statically; detonate only in the isolated lab if needed.
## Prerequisites
- `oletools` (`pip install oletools`) for `olevba`/`oleid`, or the bundled OLE/OOXML extractor.
- The document in neutralized form inside the lab.
## Safety & Handling
- Do **not** open the document in Microsoft Office. Macros may auto-run on open/close.
- Static extraction only; if dynamic analysis is required, detonate in the isolated victim VM.
## Workflow
### Step 1: Identify the container and locate macros
OOXML (`.docm`, `.xlsm`) is a ZIP; legacy (`.doc`, `.xls`) is OLE2. Macros live in a
`vbaProject.bin` OLE stream. Extract it:
```bash
python scripts/analyst.py extract maldoc.docm
```
### Step 2: Flag auto-exec triggers
Look for entry points that run without user action beyond enabling macros:
```text
AutoOpen, Document_Open, AutoClose, Workbook_Open, Auto_Open, Document_Close
```
### Step 3: Find suspicious calls
Identify execution and download primitives:
```text
Shell, WScript.Shell, CreateObject, powershell, cmd /c
URLDownloadToFile, MSXML2.XMLHTTP, ADODB.Stream (drop to disk)
Environ, GetObject("winmgmts:") (WMI)
```
### Step 4: Deobfuscate
Maldocs commonly use Chr()/string concatenation, base64, and split-and-join. Resolve these to
recover the real command and any URL or dropped path.
### Step 5: Extract IOCs and the next stage
Pull URLs, dropped file paths, and the spawned command line. These become the report's IOCs
and pivot points.
## Validation
- The recovered command line and URL make sense together (download → drop → execute).
- Auto-exec triggers explain how the macro starts.
- Deobfuscated strings match what dynamic detonation would reveal (if you confirm in the lab).
## Pitfalls
- Opening the document in Office "just to see" — this executes the macro.
- Stopping at obfuscated source without resolving it; the IOCs are inside the obfuscation.
- Missing stomped VBA (p-code present, source removed) — check for VBA stomping and analyze
p-code if the source looks empty.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the extractor and
indicator scanner.
- oletools and MS-OVBA (linked in frontmatter).

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# API Reference: Analyzing Malicious Office Macros
## Overview
`scripts/analyst.py` extracts VBA macros and flags auto-exec triggers, suspicious calls, and
URLs. It prefers `oletools`/olevba and falls back to locating `vbaProject.bin` in OOXML ZIP
containers (or scanning OLE2 bytes) when oletools is unavailable.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| oletools | >= 0.60 | VBA extraction (olevba), OLE inspection (optional but recommended) |
| (stdlib) | — | zipfile/regex fallback |
## External tools
| Tool | Purpose |
|------|---------|
| olevba / ol0id (oletools) | Macro extraction and document triage |
| oledump.py | Manual OLE stream inspection |
## Core functions
### `analyze(path)`
Dispatches to olevba or the minimal extractor; returns macro presence, auto-exec triggers,
suspicious calls, and URLs.
### `_scan_text(text)`
Flags auto-exec entry points, suspicious VBA calls, and extracts URLs from macro source.
## Usage
```bash
pip install oletools
python scripts/analyst.py extract maldoc.docm
```
## Sources
- oletools (olevba) — https://github.com/decalage2/oletools
- MS-OVBA VBA file format —
https://learn.microsoft.com/openspecs/office_file_formats/ms-ovba/
- VBA auto-exec trigger names (Office VBA object model documentation)

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#!/usr/bin/env python3
"""Extract and triage VBA macros from Office documents.
Prefers ``oletools`` (olevba) for robust extraction; falls back to locating ``vbaProject.bin``
inside OOXML (ZIP) containers and flagging suspicious indicators in the raw bytes. Never opens
the document in Office and never executes anything.
"""
from __future__ import annotations
import argparse
import json
import re
import sys
import zipfile
from pathlib import Path
AUTOEXEC = ["AutoOpen", "Document_Open", "AutoClose", "Document_Close",
"Workbook_Open", "Auto_Open", "Auto_Close"]
SUSPICIOUS = ["Shell", "WScript.Shell", "CreateObject", "powershell", "cmd",
"URLDownloadToFile", "XMLHTTP", "ADODB.Stream", "Environ",
"winmgmts", "Base64", "Chr(", "GetObject"]
def _scan_text(text: str) -> dict:
found_auto = [k for k in AUTOEXEC if k.lower() in text.lower()]
found_sus = [k for k in SUSPICIOUS if k.lower() in text.lower()]
urls = sorted(set(re.findall(r"https?://[^\s\"'<>)]+", text)))
return {"auto_exec": found_auto, "suspicious": found_sus, "urls": urls}
def extract_with_olevba(path: str) -> dict:
from oletools.olevba import VBA_Parser # type: ignore
vp = VBA_Parser(path)
macros = []
if vp.detect_vba_macros():
for _, _, name, code in vp.extract_macros():
macros.append({"name": name, **_scan_text(code),
"lines": code.count("\n") + 1})
vp.close()
return {"engine": "olevba", "has_macros": bool(macros), "macros": macros}
def extract_minimal(path: str) -> dict:
p = Path(path)
data = p.read_bytes()
indicators = {}
if zipfile.is_zipfile(path):
with zipfile.ZipFile(path) as zf:
vba = [n for n in zf.namelist() if n.lower().endswith("vbaproject.bin")]
if not vba:
return {"engine": "minimal", "has_macros": False,
"note": "no vbaProject.bin in OOXML container"}
blob = zf.read(vba[0])
# vbaProject.bin is OLE/compressed; scan recoverable ASCII for indicators
text = blob.decode("latin-1", "replace")
indicators = _scan_text(text)
return {"engine": "minimal", "has_macros": True,
"stream": vba[0], "indicators": indicators,
"note": "install oletools for full VBA source extraction"}
# legacy OLE2: scan whole file bytes for indicator strings
text = data.decode("latin-1", "replace")
return {"engine": "minimal", "has_macros": b"Attribut" in data,
"indicators": _scan_text(text),
"note": "legacy OLE2; install oletools for reliable extraction"}
def analyze(path: str) -> dict:
try:
return extract_with_olevba(path)
except ImportError:
return extract_minimal(path)
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Analyze Office macros")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("extract")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(analyze(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-malicious-onenote-and-html-smuggling
description: 'Analyzes two modern delivery techniques: malicious OneNote (.one) attachments with
embedded payloads behind fake buttons, and HTML smuggling pages that reconstruct payloads in the
browser from embedded blobs. Activates for requests to analyze a malicious OneNote file, detect
HTML smuggling, or extract a smuggled/embedded payload.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- onenote
- html-smuggling
- document-malware
- delivery
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1027.006
- T1204.002
- T1566.001
d3fend:
- D3-FCR
- D3-DA
references:
- 'MITRE ATT&CK T1027.006 HTML Smuggling — https://attack.mitre.org/techniques/T1027/006/'
- 'OneNote file format (MS-ONESTORE) — https://learn.microsoft.com/openspecs/office_file_formats/ms-onestore/'
---
# Analyzing Malicious OneNote and HTML Smuggling
## When to Use
- You have a OneNote (`.one`) attachment suspected of hiding an embedded executable/script behind a
"click to view" lure.
- You have an HTML/SVG page that reconstructs and auto-downloads a payload from an embedded blob
(HTML smuggling).
- You need to extract the hidden payload without triggering the lure.
**Do not use** OneNote or a browser to open these files for analysis — that is exactly the
delivery mechanism. Carve the embedded data statically.
## Prerequisites
- A static carver (Python) for embedded file signatures; the sample handled inertly.
## Safety & Handling
- Never open the `.one` in OneNote or the HTML in a browser.
- Defang URLs and store carved payloads password-protected.
## Workflow
### Step 1: For OneNote — carve embedded files
OneNote stores attached files in the document. Scan for embedded file signatures (`MZ`, script
headers, archives) and the `FileDataStoreObject` GUIDs, and carve them out.
```bash
python scripts/analyst.py carve sample.one
```
### Step 2: For HTML smuggling — find the embedded blob
Look for large base64/`Blob`/`Uint8Array` constructions, `data:` URIs, and a JS routine that
builds a Blob and triggers a download (`msSaveOrOpenBlob`, anchor `download`, `createObjectURL`).
### Step 3: Reconstruct the payload statically
Decode the embedded base64/byte array (and any XOR/char-code layer) to recover the payload as
data — without executing the page.
### Step 4: Analyze and extract IOCs
Hash carved payloads, identify their type, defang any URLs, and route executables/scripts to the
appropriate analysis workflow.
## Validation
- Embedded payloads are carved/reconstructed from the inert file, not by opening it.
- The recovered payload's type is identified and hashed.
- The lure/trigger mechanism (fake button, Blob download) is documented.
## Pitfalls
- Opening the OneNote/HTML to "see" the lure and executing the payload.
- Decoding only the first layer when the blob is additionally XOR/char-code encoded.
- Missing multiple embedded objects in a single OneNote page.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the carver/decoder.
- ATT&CK T1027.006 and MS-ONESTORE (linked in frontmatter).

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# API Reference: Analyzing Malicious OneNote and HTML Smuggling
## Overview
`scripts/analyst.py` carves embedded file signatures (PE, archives, scripts) from OneNote/binary
files by offset, and detects HTML smuggling by locating large base64 blobs that decode to
executables/archives alongside Blob-download hints. It reads statically and executes nothing.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | base64, regex, JSON, file IO |
## Core functions
### `carve(data)`
Returns offsets and types of embedded file signatures.
### `html_smuggle(data)`
Returns smuggling hints (Blob/Uint8Array/download) and any base64 blobs that decode to payloads.
## Usage
```bash
python scripts/analyst.py carve sample.one
```
## Sources
- MITRE ATT&CK T1027.006 HTML Smuggling — https://attack.mitre.org/techniques/T1027/006/
- OneNote file format (MS-ONESTORE, embedded FileDataStoreObject) —
https://learn.microsoft.com/openspecs/office_file_formats/ms-onestore/
- Blob/createObjectURL/msSaveOrOpenBlob smuggling pattern, established delivery-analysis practice

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#!/usr/bin/env python3
"""Carve embedded payloads from OneNote files and reconstruct HTML-smuggling blobs.
For OneNote (and any binary), it scans for embedded file signatures (PE, archives, scripts) and
reports their offsets. For HTML, it locates large base64/byte-array blobs and decodes them to
recover a smuggled payload. It reads bytes/text statically and executes nothing.
"""
from __future__ import annotations
import argparse
import base64
import binascii
import json
import re
import sys
from pathlib import Path
EMBED_SIGS = [
(b"MZ", "PE executable"),
(b"PK\x03\x04", "ZIP/Office/JAR"),
(b"\x1f\x8b", "GZIP"),
(b"Rar!\x1a\x07", "RAR"),
(b"%PDF", "PDF"),
(b"#!", "script (shebang)"),
(b"<job", "WSF job"),
]
RE_B64_BLOB = re.compile(rb"[A-Za-z0-9+/]{200,}={0,2}")
SMUGGLE_HINTS = [b"msSaveOrOpenBlob", b"createObjectURL", b"Uint8Array", b"new Blob",
b"download=", b"base64"]
def carve(data: bytes) -> list[dict]:
found = []
for sig, label in EMBED_SIGS:
start = 0
while True:
idx = data.find(sig, start)
if idx == -1:
break
found.append({"offset": idx, "type": label})
start = idx + 1
if len(found) > 50:
return found
return found
def html_smuggle(data: bytes) -> dict:
hints = sorted({h.decode() for h in SMUGGLE_HINTS if h in data})
decoded = []
for m in RE_B64_BLOB.findall(data):
try:
raw = base64.b64decode(m)
except (binascii.Error, ValueError):
continue
if raw[:2] == b"MZ" or raw[:4] == b"PK\x03\x04" or raw[:4] == b"%PDF":
decoded.append({"size": len(raw), "carved_type": carve(raw)[:1]})
return {"smuggling_hints": hints, "decoded_blobs": decoded,
"likely_smuggling": bool(hints and decoded)}
def analyze(path: str) -> dict:
data = Path(path).read_bytes()
return {
"embedded_signatures": carve(data),
"html_smuggling": html_smuggle(data),
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Carve OneNote / HTML-smuggling payloads")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("carve")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(analyze(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-malicious-pdfs
description: 'Analyzes malicious PDF documents: parsing the object structure for JavaScript,
OpenAction/launch triggers, embedded files, and URIs, then extracting and decoding suspicious
streams without opening the document in a reader. Activates for requests to analyze a malicious
PDF, inspect PDF JavaScript, or extract embedded payloads from a PDF.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- pdf
- document-malware
- javascript
- static-analysis
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1204.002
- T1059.007
- T1027
d3fend:
- D3-FCR
- D3-DA
references:
- 'PDF 32000-1:2008 (ISO PDF specification) — https://www.pdfa.org/resource/iso-32000-pdf/'
- 'Didier Stevens PDF tools (pdfid, pdf-parser) — https://blog.didierstevens.com/programs/pdf-tools/'
---
# Analyzing Malicious PDFs
## When to Use
- You have a suspicious PDF (often from phishing) and must determine if it is weaponized.
- You need to find auto-executing actions, embedded JavaScript, launch actions, or embedded files.
- You want to extract and decode suspicious streams safely.
**Do not use** a PDF reader to "just look" — opening in a viewer can trigger the very actions you
are investigating. Parse the structure statically.
## Prerequisites
- A PDF object parser (Python, or pdfid/pdf-parser/peepdf); the sample handled inertly.
## Safety & Handling
- Never open the PDF in a graphical reader; parse the raw object structure only.
- Defang any extracted URLs and store embedded payloads password-protected.
## Workflow
### Step 1: Triage the object inventory
Count high-risk keywords: `/JavaScript`, `/JS`, `/OpenAction`, `/AA`, `/Launch`, `/EmbeddedFile`,
`/URI`, `/RichMedia`. Their presence (especially auto-actions) flags weaponization.
```bash
python scripts/analyst.py triage sample.pdf
```
### Step 2: Locate auto-execution triggers
Resolve `/OpenAction` and `/AA` (additional actions) to see what runs on open, and any `/Launch`
actions that spawn external programs.
### Step 3: Extract and decode streams
Pull JavaScript and suspicious streams, applying filters (`/FlateDecode`, `/ASCIIHexDecode`) to
get the cleartext; deobfuscate layered JS as needed.
### Step 4: Recover payloads and IOCs
Extract embedded files and URIs; hash payloads, defang URLs, and route extracted executables to
PE analysis.
## Validation
- Auto-execution triggers (`/OpenAction`, `/AA`, `/Launch`) are resolved to concrete actions.
- JavaScript/streams are decoded through their filters, not left encoded.
- Embedded payloads and URIs are extracted, hashed, and defanged.
## Pitfalls
- Opening the PDF in a reader and triggering the payload.
- Missing object-stream (`/ObjStm`) compressed objects that hide the malicious content.
- Stopping at the first JS layer when it is multiply obfuscated.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the PDF triage parser.
- ISO 32000 and Didier Stevens' PDF tools (linked in frontmatter).

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# API Reference: Analyzing Malicious PDFs
## Overview
`scripts/analyst.py` scans raw PDF bytes for weaponization keywords (/JavaScript, /OpenAction,
/Launch, /EmbeddedFile, /URI, /ObjStm, etc.), counts objects/streams, extracts and defangs URIs,
and flags likely weaponization. It parses statically and never opens the document.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | regex, JSON, file IO |
## External tools
| Tool | Purpose |
|------|---------|
| pdfid / pdf-parser (Didier Stevens) | Object inventory and stream extraction with filters |
| peepdf | Interactive PDF analysis and JS extraction |
## Core functions
### `triage(path)`
Returns object/stream counts, risk-keyword counts, auto-action list, defanged URIs, and a
weaponization flag.
## Usage
```bash
python scripts/analyst.py triage sample.pdf
```
## Sources
- PDF 32000-1:2008 (ISO PDF specification) — https://www.pdfa.org/resource/iso-32000-pdf/
- Didier Stevens PDF tools (pdfid, pdf-parser) —
https://blog.didierstevens.com/programs/pdf-tools/
- /OpenAction, /AA, /Launch auto-execution and /ObjStm hiding, established PDF-analysis practice

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#!/usr/bin/env python3
"""Triage a PDF's object structure for weaponization indicators.
Scans the raw PDF bytes for high-risk keywords (/JavaScript, /OpenAction, /Launch, /EmbeddedFile,
/URI, etc.), counts indirect objects and streams, and extracts URIs. It parses bytes statically
and never opens the document in a reader, so auto-actions cannot fire.
"""
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
KEYWORDS = ["/JavaScript", "/JS", "/OpenAction", "/AA", "/Launch", "/EmbeddedFile",
"/URI", "/RichMedia", "/SubmitForm", "/GoToR", "/ObjStm", "/AcroForm",
"/XFA", "/JBIG2Decode"]
AUTO_ACTION = {"/OpenAction", "/AA", "/Launch"}
RE_URI = re.compile(rb"/URI\s*\(([^)]*)\)")
RE_OBJ = re.compile(rb"\d+\s+\d+\s+obj")
RE_STREAM = re.compile(rb"\bstream\b")
def _defang(url: str) -> str:
return url.replace("http", "hxxp").replace(".", "[.]")
def triage(path: str) -> dict:
data = Path(path).read_bytes()
if not data.lstrip().startswith(b"%PDF"):
return {"error": "not a PDF (no %PDF header)"}
counts = {kw: len(re.findall(re.escape(kw.encode()), data)) for kw in KEYWORDS}
counts = {k: v for k, v in counts.items() if v}
uris = sorted({m.decode("latin-1", "replace") for m in RE_URI.findall(data)})
auto = [kw for kw in AUTO_ACTION if counts.get(kw)]
has_js = bool(counts.get("/JavaScript") or counts.get("/JS"))
return {
"objects": len(RE_OBJ.findall(data)),
"streams": len(RE_STREAM.findall(data)),
"risk_keywords": counts,
"auto_actions": auto,
"has_javascript": has_js,
"uris": [_defang(u) for u in uris],
"weaponized_likely": bool(auto and has_js) or bool(counts.get("/Launch")),
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Triage a malicious PDF")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("triage")
p.add_argument("file")
args = parser.parse_args(argv)
print(json.dumps(triage(args.file), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-malicious-vbscript-and-wsf
description: 'Analyzes malicious VBScript, WSF, and HTA scripts: parsing WSF/HTA containers,
decoding VBScript.Encode (#@~^) and string obfuscation, and identifying WScript.Shell/ActiveX
abuse to recover dropper behavior. Activates for requests to analyze VBScript, decode a WSF/HTA,
or investigate a Windows Script Host dropper.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware-analysis
- vbscript
- wsf
- hta
- document-malware
- deobfuscation
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1059.005
- T1059.003
- T1027
d3fend:
- D3-FCR
- D3-DA
references:
- 'Microsoft Windows Script Host documentation — https://learn.microsoft.com/previous-versions/windows/internet-explorer/ie-developer/scripting-articles/'
- 'VBScript.Encode / Script Encoder format analysis — https://gist.github.com/bcse/1834878'
---
# Analyzing Malicious VBScript and WSF
## When to Use
- You have a `.vbs`, `.wsf`, or `.hta` delivered via phishing and need its real behavior.
- You must decode `VBScript.Encode`-obfuscated script (the `#@~^` marker) or string obfuscation.
- You are identifying WScript.Shell/ActiveX-based dropper actions and IOCs.
**Do not use** `wscript`/`cscript`/`mshta` to run the script for analysis — that executes the
dropper. Decode and read it statically.
## Prerequisites
- A static decoder (Python) or a script sandbox; the sample handled inertly in the lab.
## Safety & Handling
- Never run the script with Windows Script Host (`wscript`/`cscript`/`mshta`).
- Defang recovered URLs; store dropped payloads password-protected.
## Workflow
### Step 1: Parse the container
For WSF, parse the XML `<job>`/`<script>` elements (a WSF can bundle multiple languages). For HTA,
separate the HTML wrapper from the `<script>` body.
```bash
python scripts/analyst.py analyze sample.wsf
```
### Step 2: Decode VBScript.Encode
If you see the `#@~^` header, decode the Microsoft Script Encoder stream back to source.
### Step 3: Unroll string obfuscation
Resolve `Chr()`/`Asc()` builds, string concatenation, and `Replace()`-based deobfuscators to
reveal commands and URLs.
### Step 4: Identify dropper actions and IOCs
Find `CreateObject("WScript.Shell")`, `Run`/`Exec`, ADODB/MSXML download patterns, and PowerShell
hand-offs; recover URLs/paths, defang, and route payloads onward.
## Validation
- WSF/HTA containers are decomposed into their script bodies (all jobs/languages).
- `VBScript.Encode` streams are decoded to readable source.
- Dropper actions and IOCs are recovered without executing the script.
## Pitfalls
- Executing with `wscript`/`mshta` to "see" behavior, infecting the host.
- Missing one `<script>` block in a multi-job WSF.
- Overlooking the `#@~^` encoded section and analyzing only the cleartext stub.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the WSF/VBScript decoder.
- Windows Script Host docs and the Script Encoder format (linked in frontmatter).

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# API Reference: Analyzing Malicious VBScript and WSF
## Overview
`scripts/analyst.py` extracts `<script>` bodies from WSF/HTA containers, detects and isolates
Microsoft Script Encoder (`#@~^`) blocks, and flags suspicious WScript/ActiveX dropper APIs. It
parses text only and never invokes Windows Script Host. Full Script Encoder decoding is delegated
to a dedicated decoder; the script isolates encoded blocks and confirms their presence.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | regex, JSON, file IO |
## Core functions
### `analyze(text)`
Returns script-block counts, encoded-block presence, suspicious API hits, and defanged URLs.
### `_isolate_script_encoder(encoded)`
Strips the `#@~^`/`^#~@` markers and framing to isolate the encoded body for follow-on decoding.
## Usage
```bash
python scripts/analyst.py analyze sample.wsf
```
## Sources
- Microsoft Windows Script Host documentation —
https://learn.microsoft.com/previous-versions/windows/internet-explorer/ie-developer/scripting-articles/
- VBScript.Encode / Script Encoder format (`#@~^` marker) analysis —
https://gist.github.com/bcse/1834878
- WScript.Shell / ADODB.Stream / MSXML2.XMLHTTP dropper patterns, established script-analysis
practice

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#!/usr/bin/env python3
"""Analyze WSF/HTA/VBScript droppers statically.
Extracts ``<script>`` bodies from WSF/HTA XML/HTML containers, decodes Microsoft Script Encoder
(``VBScript.Encode``, ``#@~^``) streams, and flags suspicious WScript/ActiveX dropper patterns.
It parses text only and never invokes Windows Script Host.
"""
from __future__ import annotations
import argparse
import json
import re
import sys
from pathlib import Path
RE_SCRIPT = re.compile(r"<script\b[^>]*>(.*?)</script>", re.I | re.S)
RE_ENCODED = re.compile(r"#@~\^.*?\^#~@", re.S)
RE_URL = re.compile(r"https?://[^\s'\"<>)]+", re.I)
SUSPECT = ["wscript.shell", "createobject", ".run", ".exec", "powershell", "adodb.stream",
"msxml2.xmlhttp", "winhttp", "shell.application", "regwrite"]
def _isolate_script_encoder(encoded: str) -> str:
"""Isolate the body of a #@~^...^#~@ Script Encoder block for follow-on decoding.
Full Script Encoder decoding relies on a published ~140-entry substitution table; this
strips the wrapper markers and the 6-character length/checksum framing so the encoded body
can be handed to a dedicated decoder. The goal here is detection and isolation, not a
reimplementation of the entire table.
"""
m = re.match(r"#@~\^.{6}==(.*)\^#~@", encoded, re.S)
return m.group(1) if m else encoded
def analyze(text: str) -> dict:
scripts = [s.strip() for s in RE_SCRIPT.findall(text)]
if not scripts:
scripts = [text] # bare .vbs
encoded_blocks = RE_ENCODED.findall(text)
isolated = [_isolate_script_encoder(b) for b in encoded_blocks]
combined = "\n".join(scripts + isolated).lower()
indicators = sorted({s for s in SUSPECT if s in combined})
urls = sorted(set(RE_URL.findall(text)))
return {
"script_blocks": len(scripts),
"script_encoder_blocks": len(encoded_blocks),
"encoded_block_present": bool(encoded_blocks),
"suspicious_apis": indicators,
"urls": [u.replace("http", "hxxp").replace(".", "[.]") for u in urls],
"note": "encoded blocks isolated; finish Script Encoder decode with a dedicated decoder",
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Analyze VBScript/WSF/HTA")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("analyze")
p.add_argument("file")
args = parser.parse_args(argv)
text = Path(args.file).read_text(encoding="utf-8-sig", errors="replace")
print(json.dumps(analyze(text), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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---
name: analyzing-malware-in-memory-with-volatility3
description: 'Analyzes a memory image with Volatility 3 to find malware: rogue processes,
injected code, suspicious network connections, loaded modules, and persistence, then
extracts artifacts for further analysis. Activates for requests to do memory forensics,
analyze a RAM dump, or hunt malware in memory with Volatility.'
domain: cybersecurity
subdomain: malware-analysis
tags:
- malware
- memory-forensics
- volatility3
- process-injection
- dfir
- windows
version: 1.0.0
author: analyst-ai-pack
license: Apache-2.0
mitre_attack:
- T1055
- T1057
- T1003
- T1056
d3fend:
- D3-PSA
- D3-PMAD
references:
- 'Volatility 3 documentation — https://volatility3.readthedocs.io/'
- 'The Art of Memory Forensics (concepts) — https://www.memoryanalysis.net/'
---
# Analyzing Malware in Memory with Volatility 3
## When to Use
- You have a RAM image from a suspected-infected host and need to find malicious activity.
- Disk artifacts are insufficient (fileless/in-memory malware) and you need volatile evidence.
- You want to extract injected code, command lines, or network connections for analysis.
**Do not use** Volatility plugins blindly without an order of investigation — start broad
(processes, network) before deep per-process dumps.
## Prerequisites
- Volatility 3 (`pip install volatility3`) with appropriate symbol tables.
- A memory image acquired with a sound tool (WinPmem, LiME, or hypervisor snapshot).
- Knowledge of the source OS/version to select the right symbols.
## Safety & Handling
- Work on a copy of the image; preserve the original with a recorded hash.
- Treat any dumped executable region as a live sample — store and handle it accordingly.
## Workflow
### Step 1: Enumerate processes and spot anomalies
```bash
vol -f memory.raw windows.pslist
vol -f memory.raw windows.pstree
```
Look for unusual parents (Word spawning cmd/powershell), masquerading names
(`scvhost.exe`), processes with no disk path, and orphaned children.
### Step 2: Hunt injected code
```bash
vol -f memory.raw windows.malfind
```
`malfind` flags private, executable, RWX regions with no backing file — classic injection.
Note the PID and base address for dumping.
### Step 3: Review network connections
```bash
vol -f memory.raw windows.netscan
```
Correlate listening/established connections with suspicious PIDs and the C2 endpoints from
other analysis.
### Step 4: Check modules, handles, and persistence
Examine loaded DLLs (`windows.dlllist`), services, and registry (`windows.registry.*`) for
persistence and unexpected modules.
### Step 5: Dump artifacts
Dump the suspicious process or injected region for static/RE analysis:
```bash
vol -f memory.raw windows.dumpfiles --pid <pid>
```
The helper script parses Volatility's JSON renderer output to highlight injection candidates.
```bash
vol -f memory.raw -r json windows.malfind | python scripts/analyst.py malfind -
```
## Validation
- Injection candidates from `malfind` correspond to anomalous processes from `pstree`.
- Network connections map to known C2 or the sample's extracted config.
- Dumped regions disassemble into meaningful code, not random bytes.
## Pitfalls
- Wrong symbols/profile producing empty or garbage results — confirm OS build first.
- Treating every RWX region as malicious; some legitimate JIT engines use RWX. Corroborate.
- Forgetting to hash and preserve the original image before analysis.
## References
- See [`references/api-reference.md`](references/api-reference.md) for the malfind output
parser.
- Volatility 3 documentation and memory-forensics concepts (linked in frontmatter).

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# API Reference: Analyzing Malware in Memory with Volatility 3
## Overview
`scripts/analyst.py` consumes Volatility 3 `windows.malfind` JSON output and ranks memory
regions by injection signal (RWX protection, embedded MZ header, executable prologue). It
analyzes plugin output, not raw memory.
## Dependencies
| Package | Version | Purpose |
|---------|---------|---------|
| (stdlib) | — | JSON parsing |
| volatility3 | >= 2.5 | Produces the memory-plugin output this tool consumes |
## External tools
| Tool | Purpose |
|------|---------|
| Volatility 3 | Memory image analysis (pslist, pstree, malfind, netscan, dumpfiles) |
| WinPmem / LiME | Memory acquisition |
## Core functions
### `rank_malfind(doc)`
Parses malfind rows and scores each by injection indicators, returning a sorted candidate
list with PID, process, address, and protection.
## Usage
```bash
pip install volatility3
vol -f memory.raw -r json windows.malfind | python scripts/analyst.py malfind -
```
## Sources
- Volatility 3 documentation — https://volatility3.readthedocs.io/
- The Art of Memory Forensics — concepts of injection detection (malfind/RWX)
- Volatility 3 JSON renderer output structure (project docs)

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#!/usr/bin/env python3
"""Parse Volatility 3 JSON output to highlight likely process-injection candidates.
Volatility 3 can render plugin output as JSON (``vol -r json ...``). This tool consumes a
``windows.malfind`` JSON document (from a file or stdin) and ranks regions by injection
signal: RWX protection and the presence of an MZ header or executable prologue bytes.
It analyzes plugin output only; it does not parse raw memory itself.
"""
from __future__ import annotations
import argparse
import json
import sys
def _rows(doc) -> list[dict]:
# Volatility 3 JSON renderer emits a list of row dicts; tolerate a wrapped form.
if isinstance(doc, list):
return doc
if isinstance(doc, dict):
for key in ("rows", "data"):
if isinstance(doc.get(key), list):
return doc[key]
return []
def rank_malfind(doc) -> list[dict]:
out = []
for row in _rows(doc):
prot = str(row.get("Protection", row.get("protection", ""))).upper()
disasm = str(row.get("Disasm", row.get("disasm", "")))
hexdump = str(row.get("Hexdump", row.get("hexdump", "")))
score = 0
if "EXECUTE_READWRITE" in prot or "RWX" in prot:
score += 3
if "4D 5A" in hexdump or hexdump.lstrip().startswith("MZ"):
score += 3 # PE header in an injected region
if any(op in disasm for op in ("push", "mov", "call", "jmp")):
score += 1
out.append({
"pid": row.get("PID", row.get("pid")),
"process": row.get("Process", row.get("process")),
"address": row.get("Start VPN", row.get("Address", row.get("address"))),
"protection": prot,
"injection_score": score,
})
return sorted(out, key=lambda r: r["injection_score"], reverse=True)
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description="Rank Volatility malfind output")
sub = parser.add_subparsers(dest="cmd", required=True)
p = sub.add_parser("malfind")
p.add_argument("json", help="path to malfind JSON, or - for stdin")
args = parser.parse_args(argv)
text = sys.stdin.read() if args.json == "-" else open(args.json, encoding="utf-8").read()
doc = json.loads(text)
print(json.dumps(rank_malfind(doc), indent=2))
return 0
if __name__ == "__main__":
sys.exit(main())

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Apache License 2.0 — see the repository root LICENSE file for the full text.
Copyright the AnalystAIPack contributors.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

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