reverse-skills-max: initial public release (restored, redacted, hardened)
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Apache License 2.0 — see the repository root LICENSE file for the full text.
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Copyright the AnalystAIPack contributors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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---
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name: analyzing-loaders-and-droppers
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description: 'Analyzes loader and dropper samples by identifying staging behavior — embedded or
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downloaded next-stage payloads, decode/decrypt routines, and the execution mechanism used to run
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the next stage. Activates for requests to analyze a loader or dropper, find a staged payload, or
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map multi-stage delivery behavior.'
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domain: cybersecurity
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subdomain: malware-analysis
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tags:
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- malware-analysis
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- loader
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- dropper
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- multi-stage
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- delivery
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version: 1.0.0
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author: analyst-ai-pack
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license: Apache-2.0
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mitre_attack:
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- T1105
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- T1140
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- T1574
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d3fend:
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- D3-DA
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- D3-FCR
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references:
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- 'MITRE ATT&CK T1105 Ingress Tool Transfer — https://attack.mitre.org/techniques/T1105/'
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- 'MITRE ATT&CK T1140 Deobfuscate/Decode Files or Information — https://attack.mitre.org/techniques/T1140/'
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---
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# Analyzing Loaders and Droppers
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## When to Use
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- You have a first-stage loader/dropper and need to find the next stage: an embedded resource, an
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appended overlay, or a download URL.
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- You need to identify the decode/decrypt routine and the execution mechanism (process creation,
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injection, scheduled task, DLL side-loading).
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**Do not use** this to run the loader and fetch live stages — characterize staging statically and
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retrieve next stages only in an isolated sandbox.
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## Prerequisites
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- The loader/dropper sample (read inertly).
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## Safety & Handling
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- Read bytes statically; defang download URLs; never execute to pull live payloads.
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## Workflow
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### Step 1: Locate staged payloads
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```bash
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python scripts/analyst.py stage sample.bin
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```
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Detects embedded PE/archive signatures, an appended overlay beyond the PE's mapped size, large
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high-entropy blobs, and download URLs.
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### Step 2: Identify decode/execution mechanism
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Scan imports/strings for decode APIs (`CryptDecrypt`, base64), download APIs (`URLDownloadToFile`,
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`WinHttp`, `InternetReadFile`), and execution (`CreateProcess`, `ShellExecute`, `WinExec`,
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side-loading hints).
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### Step 3: Map the delivery chain
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Document stage-1 → decode → stage-2 → execution and map each step to ATT&CK.
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### Step 4: Defang and report
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Defang URLs and produce IOCs for the staging infrastructure.
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## Validation
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- Embedded/overlay stages are confirmed by signature or entropy, with offsets recorded.
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- Download URLs and execution mechanism are identified from imports/strings.
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- The multi-stage chain maps cleanly to ATT&CK techniques.
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## Pitfalls
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- Treating a benign overlay (installer data, signature) as a payload without corroboration.
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- Missing stages fetched at runtime when no URL is in plaintext (obfuscated/encoded).
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- Confusing the decode routine for the payload itself.
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## References
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- See [`references/api-reference.md`](references/api-reference.md) for the stager.
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- ATT&CK T1105 and T1140 (linked in frontmatter).
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# API Reference: Analyzing Loaders and Droppers
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## Overview
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`scripts/analyst.py` analyzes a loader/dropper: it finds embedded PE/archive signatures (skipping
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the loader's own header), counts high-entropy windows (packed/encrypted stages), and scans strings
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for download, decode, and execution APIs plus URLs. It reads bytes statically, defangs URLs, and
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executes nothing.
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## Dependencies
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| Package | Version | Purpose |
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|---------|---------|---------|
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| (stdlib) | — | math, regex, Counter, JSON, file IO |
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## Core functions
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### `stage(path)`
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Returns embedded signatures, high-entropy window count, download/decode/execution API hits,
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defanged URLs, and a loader likelihood flag.
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### `_entropy(chunk)`
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Shannon entropy (bits/byte) over a window; >7.2 suggests compressed/encrypted data.
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## API categories
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- **download**: `URLDownloadToFile`, `InternetReadFile`, WinHTTP/WinINet.
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- **decode**: `CryptDecrypt`, `CryptStringToBinary`, base64/RC4/XOR.
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- **execution**: `CreateProcess`, `ShellExecute`, `WinExec`, `CreateRemoteThread`, `LoadLibrary`.
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## Usage
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```bash
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python scripts/analyst.py stage sample.bin
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```
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## Sources
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- MITRE ATT&CK T1105 Ingress Tool Transfer — https://attack.mitre.org/techniques/T1105/
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- MITRE ATT&CK T1140 Deobfuscate/Decode Information — https://attack.mitre.org/techniques/T1140/
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- Shannon entropy definition; overlay/staging patterns from public loader analyses
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#!/usr/bin/env python3
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"""Analyze a loader/dropper: locate staged payloads and identify decode/download/execution APIs.
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Detects embedded PE/archive signatures, scans strings for download/decode/execution APIs and
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URLs, and computes Shannon entropy over windows to flag large high-entropy (packed/encrypted)
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blobs. Reads bytes statically; executes nothing.
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"""
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from __future__ import annotations
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import argparse
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import json
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import math
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import re
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import sys
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from collections import Counter
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from pathlib import Path
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ASCII_RE = re.compile(rb"[\x20-\x7e]{4,}")
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UTF16_RE = re.compile(rb"(?:[\x20-\x7e]\x00){4,}")
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RE_URL = re.compile(r"https?://[^\s\"'<>]+", re.I)
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EMBED_SIGS = [(b"MZ", "PE"), (b"PK\x03\x04", "ZIP/Office"), (b"Rar!\x1a\x07", "RAR"),
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(b"\x1f\x8b", "GZIP"), (b"7z\xbc\xaf\x27\x1c", "7z")]
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DOWNLOAD_API = ["urldownloadtofile", "internetreadfile", "internetopenurl", "winhttp",
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"httpopenrequest", "wininet"]
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DECODE_API = ["cryptdecrypt", "cryptstringtobinary", "base64", "rc4", "xor"]
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EXEC_API = ["createprocess", "shellexecute", "winexec", "createremotethread", "loadlibrary",
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"ntcreateuserprocess", "wow64"]
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def _strings(data: bytes) -> str:
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parts = [m.decode("ascii", "ignore") for m in ASCII_RE.findall(data)]
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parts += [m.decode("utf-16-le", "ignore") for m in UTF16_RE.findall(data)]
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return "\n".join(parts)
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def _entropy(chunk: bytes) -> float:
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if not chunk:
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return 0.0
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counts = Counter(chunk)
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total = len(chunk)
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return -sum((c / total) * math.log2(c / total) for c in counts.values())
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def stage(path: str) -> dict:
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data = Path(path).read_bytes()
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embedded = []
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for sig, label in EMBED_SIGS:
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idx = data.find(sig, 1) # skip offset 0 (the loader's own header)
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if idx != -1:
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embedded.append({"offset": idx, "type": label})
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win = 4096
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high_entropy = sum(1 for i in range(0, len(data), win)
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if _entropy(data[i:i + win]) > 7.2)
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blob = _strings(data)
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low = blob.lower()
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urls = sorted({u.replace("http", "hxxp").replace(".", "[.]") for u in RE_URL.findall(blob)})
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return {
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"embedded_signatures": embedded,
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"high_entropy_windows": high_entropy,
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"download_apis": sorted({a for a in DOWNLOAD_API if a in low}),
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"decode_apis": sorted({a for a in DECODE_API if a in low}),
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"execution_apis": sorted({a for a in EXEC_API if a in low}),
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"urls": urls[:50],
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"is_likely_loader": bool(embedded) or bool(urls and any(a in low for a in DOWNLOAD_API)),
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}
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def main(argv: list[str] | None = None) -> int:
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parser = argparse.ArgumentParser(description="Analyze loader/dropper staging")
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sub = parser.add_subparsers(dest="cmd", required=True)
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p = sub.add_parser("stage")
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p.add_argument("file")
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args = parser.parse_args(argv)
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print(json.dumps(stage(args.file), indent=2))
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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