SkillAudit report — perplexityai/modelcontextprotocol

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

Commit: dd5e078 · Stars: 2094 · Days since last push: 9

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

Overall grade: C (75/100)

Security findings

Production sources:

• WARN src/server.ts:38 — HTTP client call with user-controlled argument 'url' (validation markers present but not verified against this call-site)

return fetch(url, options);

Test-site findings (lower weight): 5 total in test/ paths — first 3 shown

• HIGH src/transport.test.ts:130 — Template-string URL with interpolation — no validation possible on composed string

const response = await fetch(\http://localhost:${port}/mcp\, {

• HIGH src/transport.test.ts:202 — Template-string URL with interpolation — no validation possible on composed string

const response = await fetch(\http://localhost:${port}/mcp\, {

• HIGH src/transport.test.ts:246 — Template-string URL with interpolation — no validation possible on composed string

const response = await fetch(\http://localhost:${port}/mcp\, {

Permissions

_No findings on this axis._

Credentials

_No findings on this axis._

Maintenance

_No findings on this axis._

Compatibility

_No findings on this axis._

Documentation

Production sources:

• WARN (meta) — No SECURITY.md — no disclosure channel for vulnerabilities

missing

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_