MCPwner: Model Context Protocol server for autonomous vulnerability discovery

by Pigyon · MCP Server · ★ 56

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

How to install MCPwner

git clone https://github.com/nedlir/mcpwner.git

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 62/100

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

Stars56
Forks8
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score62/100
Last Updated2026-08-20
Created2026-01-23
Platformsmcp, python
Est. Tokens~19k

About MCPwner

MCPwner Beware the Badger Model Context Protocol server for security research automation Compatible with: Overview MCPwner is a Model Context Protocol (MCP) server that integrates security testing tools into LLM-driven workflows. It provides a unified interface for secret scanning, static analysis (SAST), software composition analysis (SCA), infra

Compatible Skills

These tools work well together with MCPwner for enhanced workflows:

  • plane-mcp-server — semantic(0.55)+same_lang+similar_pop+shared_platform (54%)
  • sec-edgar-mcp — semantic(0.53)+same_lang+similar_pop+shared_platform (54%)
  • LLM-SR — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
  • llm-srbench — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)
  • AI-Powered-Vulnerability-Impact-Analyzer — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (51%)

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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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