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mrphrazer/ghidra-headless-mcp

Headless Ghidra MCP server — giving AI agents deep reverse-engineering capabilities.

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  • Python
  • ★ 198
  • commit 307b461cdbbe
  • scored 2026-11-13
  • scoring prov-4+qual-2+sec-2

AI provenance

0%

possible

Confidence
43%
Coverage
100%

Estimated share of AI-generated code. Explicit attribution is a lower bound.

Code quality

65/100

weak

Confidence
90%
Coverage
100%

Complexity, duplication, lint density, tests, CI, docs and review process.

Security

78/100

adequate

Confidence
85%
Coverage
100%

Secrets, static analysis, vulnerable dependencies, CI and supply-chain posture.

Scores

Pillar Score Signal Confidence Coverage
AI provenance (estimated AI-generated share) 0 possible 43% 100%
Code quality 65 weak 90% 100%
Security 78 adequate 85% 100%

AI provenance

Weak signals consistent with AI-assisted development.

  • Explicitly attributed share (lower bound): 0.0%
  • Unattributed share in AI-consistent velocity bursts: 9.9%
  • Estimated AI-generated share: 0.0%
Month Lines added Attributed to AI
2026-03 18,062 0%
2026-05 68 0%
2026-06 2,082 0%
2026-07 91 0%
2026-08 806 0%

Evidence

  • 2,082 lines of first-party code in ~4.0h (520 lines/hour, 1 commit session) (10a40eb99bdf)

AI provenance describes how code was produced; it is not a quality judgment. Unless attribution is explicit, these are signals consistent with AI generation, not proof. Squash/rebase workflows, imported code, and rewritten history reduce accuracy.

Code quality

Code quality is weak: test-to-source ratio 0.26, CI runs tests; 29% of function code in complex or long functions; 10% duplicated lines. Weakest area: type safety.

Area Score
Complexity (lizard) 42
Duplication 56
Code smells (Opengrep) 73
Tests and CI 88
Documentation 85
Type safety 0
Process (Scorecard review/maintenance) n/a
  • Functions: 702; mean complexity 4.0, 90th percentile 7; 20 complex and 16 long functions
  • Duplicated lines: 9.5% of 13,622 compared
  • Tests: 4,251 test lines for 16,599 source lines (ratio 0.26); CI test commands: pytest; 75% of recent source commits also change tests
  • Docs missing or thin: contributing

Most complex functions:

Security

Security posture is adequate: 1 high-severity SAST findings; 2 high-severity workflow issues. Weakest area: posture.

Area Score
Secrets (gitleaks, tree + history) 100
Static analysis (Opengrep) 96
Vulnerable dependencies (OSV) n/a
CI workflows (zizmor) 67
Posture (policy, updates, signing, lockfiles) 0
OpenSSF Scorecard n/a
  • Posture checks passed: lockfiles; missing: security policy, dependency updates, signed commits

Top findings

Severity Finding Location
high opengrep/python.lang.security.audit.dangerous-subprocess-use-audit: Detected subprocess function 'Popen' without a static string. If this data can be controlled by a malicious actor, it may be an instance of command injection. Audit the use of this call to ensure it is not controllable by an external resource. You may consider using 'shlex.escape()'. [confidence low] ghidra_headless_mcp/ghidra_cli.py:839
high zizmor/unpinned-uses: unpinned action reference [confidence high] .github/workflows/ci.yml:17
high zizmor/unpinned-uses: unpinned action reference [confidence high] .github/workflows/ci.yml:20
medium opengrep/python.lang.security.audit.exec-detected: Detected the use of exec(). exec() can be dangerous if used to evaluate dynamic content. If this content can be input from outside the program, this may be a code injection vulnerability. Ensure evaluated content is not definable by external sources. [confidence low] ghidra_headless_mcp/backend.py:3145
medium opengrep/python.lang.security.audit.eval-detected: Detected the use of eval(). eval() can be dangerous if used to evaluate dynamic content. If this content can be input from outside the program, this may be a code injection vulnerability. Ensure evaluated content is not definable by external sources. [confidence low] ghidra_headless_mcp/backend.py:3148
medium opengrep/python.lang.security.audit.exec-detected: Detected the use of exec(). exec() can be dangerous if used to evaluate dynamic content. If this content can be input from outside the program, this may be a code injection vulnerability. Ensure evaluated content is not definable by external sources. [confidence low] ghidra_headless_mcp/fake_ghidra.py:1193
medium opengrep/python.lang.security.audit.eval-detected: Detected the use of eval(). eval() can be dangerous if used to evaluate dynamic content. If this content can be input from outside the program, this may be a code injection vulnerability. Ensure evaluated content is not definable by external sources. [confidence low] ghidra_headless_mcp/fake_ghidra.py:1196
low zizmor/artipacked: credential persistence through GitHub Actions artifacts [confidence low] .github/workflows/ci.yml:16

Static signals only: the scorer never runs the project's code. Secret findings are pattern matches that were not verified, and secret values are never stored.

Repository profile

  • Source lines: 16,599; test lines: 4,251
Language Lines
Python 20,840
C 10

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