ghidra-headless-mcp: Headless Ghidra MCP server — giving AI agents deep reverse-engineering capabilities

by mrphrazer · MCP Server · ★ 191

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 72/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

Quick Facts

Stars191
Forks23
LanguagePython
CategoryMCP Server
LicenseGPL-2.0
Quality Score72/100
Last Updated2026-09-16
Created2026-03-08
Platformsmcp, python
Est. Tokens~21k

About ghidra-headless-mcp

Ghidra Headless MCP A headless Ghidra server that speaks MCP (Model Context Protocol), giving AI agents full access to deep reverse-engineering workflows: program lifecycle, disassembly, decompilation, patching, types, xrefs, scripting, and more, without a GUI. Designed to run in the same Docker container as the agent runtime. No sidecars, no extra services.

Compatible Skills

These tools work well together with ghidra-headless-mcp for enhanced workflows:

  • ai-reverse-engineering — semantic(0.58)+complementary+same_lang+similar_pop+shared_platform (70%)
  • decyx — semantic(0.50)+complementary+same_lang+similar_pop+shared_platform (68%)
  • ghidra-rpc — semantic(0.48)+complementary+same_lang+similar_pop+shared_platform (67%)
  • ghidra-re-skill — semantic(0.42)+complementary+same_lang+similar_pop+shared_platform (65%)
  • auto-re-agent — semantic(0.40)+complementary+same_lang+similar_pop+shared_platform (64%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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