SkillAudit report — jlowin/fastmcp

Scanned 2026-04-24 by SkillAudit v0.2 (static checks + LLM-assisted prompt-injection red-team).

Commit: e95efce · Stars: 24813 · Days since last push: 0

LLM prompt-injection probe: skipped — set ANTHROPIC_API_KEY to enable the LLM-assisted prompt-injection red-team

Overall grade: F (35/100)

Security findings

Production sources:

• HIGH src/fastmcp/cli/cimd.py:185 — HTTP client call with user-controlled argument 'url' — no URL allowlist / validation found in file

doc = await fetcher.fetch(url)

• HIGH src/fastmcp/server/auth/cimd.py:737 — HTTP client call with user-controlled argument 'client_id_url' — no URL allowlist / validation found in file

cimd_doc = await self._fetcher.fetch(client_id_url)

Test-site findings (lower weight): 24 total in test/ paths — first 3 shown

• HIGH tests/server/auth/test_cimd.py:234 — HTTP client call with user-controlled argument 'url' — no URL allowlist / validation found in file

doc = await fetcher.fetch(url)

• HIGH tests/server/auth/test_cimd.py:252 — HTTP client call with user-controlled argument 'url' — no URL allowlist / validation found in file

first = await fetcher.fetch(url)

• HIGH tests/server/auth/test_cimd.py:253 — HTTP client call with user-controlled argument 'url' — no URL allowlist / validation found in file

second = await fetcher.fetch(url)

Permissions

_No findings on this axis._

Credentials

Test-site findings (lower weight): 1 total in test/ paths — first 3 shown

• HIGH tests/integration_tests/auth/test_github_provider_integration.py:382 — Hardcoded GitHub OAuth token found in source

gho_*** (GitHub OAuth token, 33 chars)

Maintenance

Production sources:

• WARN (meta) — 224 open issues — triage backlog

224 open

Compatibility

_No findings on this axis._

Documentation

_No findings on this axis._

Methodology

SkillAudit v0.2 clones the repo at the provided ref (default: default branch, HEAD) into an ephemeral sandbox, runs six static checks over .js/.ts/.py sources, queries the GitHub API for maintenance signals, and runs an LLM-assisted prompt-injection red-team over the MCP tool surface. Each axis is scored against the rubric at .

The prompt-injection axis extracts each server.tool(...) / @app.tool registration + the first ~60 lines of handler body, hands them to Claude Haiku 4.5 with a red-team system prompt, and asks for structured findings on untrusted-content flow into tool responses. One API call per scan, bounded at ~15K input tokens.

How to improve this grade

• Security — static: validate tool-input URLs against an allowlist before fetch/axios calls; use execFile with argv arrays instead of exec with template strings; never pass untrusted strings to subprocess with shell=True.
• Security — prompt injection: never return fetched web-page / file / email content verbatim in a tool response. Wrap with a framing marker (e.g., <untrusted-content>...</untrusted-content>), summarize rather than inline, and never let untrusted content share a turn with credentials or other tool output.
• Credentials findings: redact env-var reads before log lines and error messages; treat any string that ends up in a tool response as public.
• Maintenance: if the repo is inactive, document the maintenance model — "MCP tool, no breaking changes expected" is a legitimate signal.
• Docs: add a README install + usage section with a copy-pasteable command; add a SECURITY.md with a disclosure channel.

_Report generated by skillaudit.dev_