reverse-skills-max/vendor-new/analyst-ai-pack/skills/reverse-engineering-binaries-with-binary-ninja/SKILL.md

name, description, domain, subdomain, tags, version, author, license, mitre_attack, d3fend, references

name	description	domain	subdomain	tags	version	author	license	mitre_attack	d3fend	references
reverse-engineering-binaries-with-binary-ninja	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. Activates for requests to reverse a binary with Binary Ninja, script the Binary Ninja API, or work with its intermediate languages.	cybersecurity	reverse-engineering	reverse-engineering binary-ninja il automation static-analysis	1.0.0	analyst-ai-pack	Apache-2.0	T1027 T1059 T1587.001	D3-DA D3-SDA	Binary Ninja Python API documentation — https://api.binary.ninja/ Binary Ninja IL (LLIL/MLIL/HLIL) guide — https://docs.binary.ninja/dev/bnil-llil.html

Reverse Engineering Binaries With Binary Ninja

When to Use

• You want to reverse a binary with Binary Ninja and automate analysis via its Python API (function enumeration, IL traversal, annotation, extraction).
• You need to leverage MLIL/HLIL for cleaner analysis of obfuscated code.

Do not use the headless API to execute the sample — Binary Ninja performs static analysis. Run in an isolated environment and treat inputs as malicious.

Prerequisites

• Binary Ninja with the binaryninja Python API available (the script degrades gracefully and generates a script skeleton if the API is not importable).

Safety & Handling

• Static analysis does not execute the sample; keep inputs isolated.

Workflow

Step 1: Generate an analysis script skeleton

python scripts/analyst.py skeleton --emit functions,strings --out bn_extract.py

Emits a Binary Ninja Python script that opens a view, iterates functions, and exports functions/strings to JSON using the real API (open_view, bv.functions, bv.get_strings).

Step 2: Choose the IL level

Use LLIL for close-to-assembly, MLIL for variable/SSA reasoning, and HLIL for readable pseudo-code; traverse instructions and operands programmatically.

Step 3: Automate annotation

Rename symbols (func.name), add comments (bv.set_comment_at), and create tags for findings.

Step 4: Run and aggregate

Execute the script (headless or in the UI console) and aggregate the JSON output.

Validation

• The skeleton uses real API calls (binaryninja.open_view, bv.functions).
• The chosen IL level matches the analysis goal.
• Exported JSON contains plausible functions/strings.

Pitfalls

• Headless licensing differences (Commercial vs Personal) affecting open_view availability.
• Confusing IL levels and operand structures across LLIL/MLIL/HLIL.
• Long analysis times on large binaries — scope function ranges.

References

• See references/api-reference.md for the skeleton generator.
• Binary Ninja API and IL docs (linked in frontmatter).