mcp-riskmap 0.1.5

Local-first static auditor for risky MCP and agent-tool repository patterns.

mcp-riskmap

mcp-riskmap is a local-first static auditor for MCP and agent-tool repositories. It looks for risky MCP configuration, shell-enabled tool handlers, prompt-injection-like tool descriptions, and missing maintainer guidance without starting untrusted MCP servers.

This project is intentionally small and conservative. It is designed for maintainers who want a quick review signal in local development, pull requests, and GitHub Code Scanning.

Why this exists

MCP servers often expose tools that can touch files, shells, networks, credentials, or local developer state. Reference servers and community examples are useful, but each maintainer still needs a threat model and basic safeguards before sharing configs or accepting tool changes.

mcp-riskmap focuses on static signals that are cheap to review:

• MCP config that starts through cmd, powershell, bash, or sh
• Remote install pipelines such as curl ... | sh or curl ... | iex
• Secret-like environment variables passed into MCP servers
• Full process environment passthrough into Python or JavaScript child processes
• Python subprocess(..., shell=True), os.system, eval, and exec
• JavaScript child_process.exec and spawn(..., { shell: true })
• Python and JavaScript filesystem access that appears to use user-controlled path input
• Tool text that looks like model-control prompt injection
• Missing AGENTS.md, SECURITY.md, or LICENSE

Install

From PyPI:

python -m pip install --upgrade mcp-riskmap

From a checkout:

python -m pip install -e .

From GitHub:

python -m pip install "git+https://github.com/vawkdh-job/mcp-riskmap.git@v0.1.5"

For development without installing:

$env:PYTHONPATH = "src"
python -m mcp_riskmap.cli scan examples/unsafe-mcp-server
python -m mcp_riskmap.cli --version

Usage

mcp-riskmap scan .
mcp-riskmap scan . --format json
mcp-riskmap scan . --format markdown --output report.md
mcp-riskmap scan . --format sarif --output results.sarif --fail-on high
mcp-riskmap scan . --exclude "examples/**" --exclude "tests/**"

--fail-on high returns exit code 1 when at least one finding is high or critical.

Use --exclude for reviewed fixture directories, generated output, or intentionally unsafe examples that should not block CI.

Examples

• examples/unsafe-mcp-server/ contains intentionally risky MCP config and tool-handler patterns for scanner demonstrations.
• examples/safe-mcp-server/ contains a safer file-read pattern using a resolved base directory boundary check.
• docs/rules.md contains unsafe examples, safer patterns, and review notes for each rule.

Example output

SEVERITY  RULE                       LOCATION      MESSAGE
--------  -------------------------  ------------  ------------------------------------------------
CRITICAL  MCP-CONFIG-REMOTE-INSTALL  mcp.json:5    MCP server 'unsafe-demo' downloads and executes...
HIGH      PY-SHELL-TRUE              server.py:6   A Python tool handler can pass input through a shell.
HIGH      JS-CHILD-PROCESS-EXEC      server.js:4   A JavaScript tool handler can pass input through a shell.

Output formats

• table: compact terminal output
• json: automation-friendly structured output
• markdown: issue and release-note friendly report
• sarif: GitHub Code Scanning compatible output

Structured outputs redact secret-like evidence values before writing JSON or SARIF.

Reviewed suppressions

If a maintainer reviews a finding and accepts the risk, add a narrow suppression on the same line or the previous line:

# mcp-riskmap: ignore PY-SHELL-TRUE
subprocess.run(command, shell=True)

Use rule-specific suppressions where possible. mcp-riskmap: ignore suppresses all rules on the next line and should be reserved for generated or documented fixture code.

GitHub Action

This repository includes a composite GitHub Action:

name: mcp-riskmap

on:
  pull_request:
  push:
    branches: [main]

jobs:
  scan:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      security-events: write
    steps:
      - uses: actions/checkout@v6
      - uses: vawkdh-job/mcp-riskmap@v0.1.5
        with:
          path: .
          format: sarif
          output: mcp-riskmap.sarif
          fail-on: high
          exclude: |
            examples/**
            tests/**
      - uses: github/codeql-action/upload-sarif@v3
        if: always()
        with:
          sarif_file: mcp-riskmap.sarif

Safety stance

mcp-riskmap does not execute MCP servers. It reads files and reports static findings. That means it will miss runtime-only behavior, but it is safer for quick review of unknown configs and pull requests.

Why static only?

Some MCP scanners inspect live tool descriptions by starting configured servers. That can be useful, but it is risky when a reviewer is looking at an unknown repository or pull request. mcp-riskmap is meant to run earlier in the review flow: it reads files, flags obvious risk, and produces review artifacts without executing commands from the target project.

Compared with dynamic scanners

mcp-riskmap is not a replacement for dynamic MCP inspection. It is a first-pass guardrail for maintainers who need quick review signals in CI and code review.

Area	mcp-riskmap	Dynamic scanners
Starts scanned MCP servers	No	Often yes
Safe for unknown PRs	Designed for this	Depends on sandboxing
CI/SARIF friendly	Yes	Depends on tool
Runtime behavior coverage	Limited	Better
Static source/config review	Primary focus	Varies

Current limitations

• Rules are conservative regex/static checks, not full taint analysis.
• The scanner does not inspect live MCP tool responses.
• Secret detection is key-name based and does not do high-entropy scanning.
• JavaScript and Python analyzers focus on common high-risk patterns first.

Roadmap

• Add CI examples for consuming mcp-riskmap from other repositories.
• Add rule severity profiles.
• Add Semgrep-compatible pattern export.

See ROADMAP.md for issue-sized milestones.

OpenAI Codex for OSS fit

This project is intended to be maintained as an open-source security and maintainer automation tool. It includes tests, CI, SARIF output, GitHub Code Scanning support, a published PyPI package, examples, security docs, contribution guidance, AGENTS.md, protected-main workflow notes, and tagged releases.

Codex/API credits would be useful for reviewing rule changes, generating regression tests, triaging issues, improving documentation, and producing release notes. AI output should be reviewed by maintainers before merge.

See docs/codex-for-oss.md for application-specific maintainer workflow notes.