The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
MCP Audit
See what your AI agents can access. Scan MCP configs for exposed secrets, shadow APIs, and AI models. Generate AI-BOMs for compliance.
Install
docker run -v $(pwd):/scan mcp-audit scanclaude mcp add mcp-audit -- docker run -v $(pwd):/scan mcp-audit scan{
"mcpServers": {
"mcp-audit": {
"command": "docker",
"args": [
"run",
"-v",
"$(pwd):/scan",
"mcp-audit",
"scan"
]
}
}
}Install command taken from the project's README or registry entry. Always review code before running it.
README excerpt
Read full README on GitHub →MCP Audit
See what your AI agents can access - before they go live.
Web App
CLI
Quick Start
# Install
pip install -e .
# Scan your machine
mcp-audit scan
# Or try the web app (no install)
# https://apisec-inc.github.io/mcp-audit/?demo=true
What It Does
MCP Audit scans your AI development tools (Claude Desktop, Cursor, VS Code) and reveals:
- Secrets - Exposed API keys, tokens, database passwords
- APIs - Every endpoint your AI agents connect to
- AI Models - Which LLMs are configured (GPT-4, Claude, Llama)
- Risk Flags - Shell access, filesystem access, unverified sources
⚠️ 2 SECRET(S) DETECTED - IMMEDIATE ACTION REQUIRED
[CRITICAL] GitHub Personal Access Token
Location: github-tools → env.GITHUB_TOKEN
Remediation: https://github.com/settings/tokens → Delete → Recreate
[HIGH] Database Connection String
Location: postgres-mcp → env.DATABASE_URL
Remediation: Rotate credentials, use secrets manager
Source-level Scanning (new in v1.1)
mcp-audit scan inventories MCP server configurations. The new mcp-audit source-scan command goes one level deeper — it reads the MCP server's own source code and flags code-level vulnerabilities the server author may have introduced.
Today it catches the "Prompt In, Shell Out" attack chain: an MCP server that pipes an LLM-controlled tool argument into a shell-spawning API (child_process.exec, util.promisify(exec), subprocess.run(shell=True), os.system, os.popen) without sanitization. An attacker controlling the LLM input can inject shell metacharacters and execute arbitrary code on the host running the MCP server.
$ mcp-audit source-scan ./packages/my-mcp-server
MCP source-scan: packages/my-mcp-server
1 critical · 0 high · 1 finding(s) total
Severity Conf File:Line API Snippet
CRITICAL high server.js:19 util.promisify(child_process.exec) ... const { stdout } = await execAsync(
Outputs:
--format table(default, human-readable)--format json(CI integrations, jq pipelines)--format sarif(upload to GitHub code-scanning / GitLab / similar)
Gate merges on critical findings:
mcp-audit source-scan ./my-mcp --exit-code
The scanner is intentionally narrow — it only opens files that look like MCP server source (imports an MCP SDK). It won't try to find shell-injection bugs in random Node / Python code; that's not the job.
After running mcp-audit scan, you'll see a nudge in the summary suggesting source-scan against any in-house or unverified servers it discovers.
What It Finds (and Doesn't Find)
What It Finds
| Scan Type | Finds |
|---|---|
| GitHub Scan | MCP configs committed to repositories (mcp.json, .mcp/, claude_desktop_config.json, etc.) |
| Local Scan | MCP configs on your machine (Claude Desktop, Cursor, VS Code, Windsurf, Zed) |
| Source Scan (v1.1) | Code-level vulnerabilities in MCP server source (JS/TS/Python): shell-injection sinks (child_process.exec, util.promisify(exec), subprocess.run(shell=True), os.system, os.popen) called with interpolated tool arguments |
What It Won't Find
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