SkillAudit report — resend/mcp-send-email
Scanned 2026-04-24 by SkillAudit v0.2 (static checks + LLM-assisted prompt-injection red-team).
Commit: c90b062 · Stars: 504 · Days since last push: 1
LLM prompt-injection probe: skipped — set ANTHROPIC_API_KEY to enable the LLM-assisted prompt-injection red-team
Overall grade: F (35/100)
| Axis | Score | Grade | |
|---|---|---|---|
| security | 35/100 | F | ❌ |
| permissions | 100/100 | A | ✅ |
| credentials | 90/100 | A | ✅ |
| maintenance | 100/100 | A | ✅ |
| compatibility | 100/100 | A | ✅ |
| docs | 90/100 | A | ✅ |
Security findings
Production sources:
- HIGH
src/lib/dashboard-client.ts:12— HTTP client call with user-controlled argument 'url' — no URL allowlist / validation found in file
const response = await fetch(url);
- HIGH
src/lib/resend-editor-client.ts:18— HTTP client call with user-controlled argument 'url' — no URL allowlist / validation found in file
const response = await fetch(url, {
Test-site findings (lower weight): 1 total in test/ paths — first 3 shown
- HIGH
tests/transports/http.test.ts:30— Template-string URL with interpolation — no validation possible on composed string
const res = await fetch(\http://127.0.0.1:${port}/health\);
Permissions
_No findings on this axis._
Credentials
Production sources:
- WARN
.env.example— .env file present in repo tree — verify it's a template, not real secrets
.env.example
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
execFilewith argv arrays instead ofexecwith template strings; never pass untrusted strings tosubprocesswithshell=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_
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