F

Public audit · 2026-04-24

neo4j-contrib/mcp-neo4j

Overall: F (10/100) · v0.2 scan · 6 axes · LLM prompt-injection probe

SkillAudit report — neo4j-contrib/mcp-neo4j

Scanned 2026-04-24 by SkillAudit v0.2 (static checks + LLM-assisted prompt-injection red-team).
Commit: dbc01ba · Stars: 940 · Days since last push: 14
LLM prompt-injection probe: no-tool-surface

Overall grade: F (10/100)

AxisScoreGrade
security10/100F
permissions100/100A
credentials100/100A
maintenance100/100A
compatibility70/100C⚠️
docs90/100A

Security findings

Production sources:

response = requests.post(auth_url, headers=headers, data=payload)

response = requests.get(url, headers=self._get_headers())

response = requests.get(url, headers=self._get_headers())

response = requests.get(url, headers=self._get_headers())

response = requests.post(url, headers=self._get_headers(), json=payload)

response = requests.post(url, headers=self._get_headers())

response = requests.post(url, headers=self._get_headers())

response = requests.get(url, headers=self._get_headers())

response = requests.get(url, headers=self._get_headers())

response = requests.delete(url, headers=self._get_headers())

Test-site findings (lower weight): 2 total in test/ paths — first 3 shown

response = requests.get(server_url.replace("/mcp/", "/health"), timeout=5)

response = requests.get(url, timeout=5)

Permissions

_No findings on this axis._

Credentials

_No findings on this axis._

Maintenance

_No findings on this axis._

Compatibility

Production sources:

Documentation

Production sources:

no usage section


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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