SkillAudit report — JetBrains/mcp-jetbrains

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

Commit: c8c2b85 · Stars: 949 · Days since last push: 106

LLM prompt-injection probe: no-tool-surface

Overall grade: F (10/100)

Security findings

Production sources:

• HIGH src/index.ts:61 — Template-string URL with interpolation — no validation possible on composed string

const res = await fetch(\${endpoint}/mcp/list_tools\);

• HIGH src/index.ts:179 — Template-string URL with interpolation — no validation possible on composed string

const toolsResponse = await fetch(\${cachedEndpoint}/mcp/list_tools\);

• HIGH src/index.ts:205 — Template-string URL with interpolation — no validation possible on composed string

const response = await fetch(\${cachedEndpoint}/mcp/${name}\, {

Permissions

_No findings on this axis._

Credentials

Production sources:

• HIGH src/index.ts:103 — Error message includes env-var value — propagates to caller and LLM

throw new Error(\Specified IDE_PORT=${process.env.IDE_PORT} but it is not responding correctly.\);

Maintenance

_No findings on this axis._

Compatibility

Production sources:

• WARN (meta) — No engines (Node) or python_requires declared — cross-client compatibility unverified

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_