F

Public audit · 2026-04-24

getsentry/sentry-mcp

Overall: F (0/100) · v0.2 scan · 6 axes · LLM prompt-injection probe

SkillAudit report — getsentry/sentry-mcp

Scanned 2026-04-24 by SkillAudit v0.2 (static checks + LLM-assisted prompt-injection red-team).
Commit: 6bd4a2e · Stars: 667 · Days since last push: 0
LLM prompt-injection probe: no-tool-surface

Overall grade: F (0/100)

AxisScoreGrade
security0/100F
permissions100/100A
credentials5/100F
maintenance100/100A
compatibility100/100A
docs100/100A

Security findings

Production sources:

const resp = await fetch(upstream_url, {

const response = await fetch(registrationUrl, {

const response = await fetch(tokenUrl, {

const response = await fetch(tokenUrl, {

const response = await fetch(

response = await fetch(url, {

private async request(

const response = await this.request(path, options, requestOptions);

const downloadResponse = await this.request(

return fetch(requestUrl.toString(), {

Test-site findings (lower weight): 6 total in test/ paths — first 3 shown

const validResponse = await fetch(

const invalidFieldsResponse = await fetch(

const invalidSortResponse = await fetch(

Permissions

_No findings on this axis._

Credentials

Production sources:

sk-*** (OpenAI / Anthropic-style API key, 22 chars)

sk-*** (OpenAI / Anthropic-style API key, 21 chars)

.env.example

packages/mcp-cloudflare/.env.example

packages/mcp-test-client/.env.test

Test-site findings (lower weight): 11 total in test/ paths — first 3 shown

sk-*** (OpenAI / Anthropic-style API key, 51 chars)

sk-*** (OpenAI / Anthropic-style API key, 51 chars)

sk-*** (OpenAI / Anthropic-style API key, 51 chars)

Maintenance

_No findings on this axis._

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

_Report generated by skillaudit.dev_

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