Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AI-first security scanning with Medusa. 3,000+ detection patterns covering AI/ML, agents, MCP, RAG, prompt injection, and traditional SAST vulnerabilities. Wraps Medusa CLI with SARIF/JSON parsing, structured finding output, OWASP mapping, and remediation guidance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
AI-first security scanner integration skill. Leverages Medusa's 76 scanners and 3,000+ detection patterns for comprehensive security analysis including AI/ML-specific vulnerability detection.
Python 3.10+
pip install medusa-security
Check installation: python -m medusa --version
# Step 1: Verify installation
python -m medusa --version
# Step 2: Run scan
medusa scan . --format sarif --fail-on high
# Step 3: Parse output (use scripts/main.cjs)
node .claude/skills/medusa-security/scripts/main.cjs --mode full --target .
# Step 4: Review findings by severity
# CRITICAL → immediate fix required
# HIGH → fix before release
# MEDIUM → fix in next sprint
# LOW → track and address
medusa scan . --format sarif --ai-only
Scans only: prompt injection (800+ patterns), MCP security (400+ patterns), agent security (500+ patterns), RAG security (300+ patterns).
medusa scan . --format sarif --quick
Only scans git-changed files. Use during development for rapid feedback.
# MCP security only
medusa scan . --format sarif --scanners mcp-server,mcp-config
# Secrets only
medusa scan . --format sarif --scanners secrets,gitleaks,env
# AI context files only
medusa scan . --format sarif --scanners ai-context
The skill uses helper scripts located at .claude/skills/medusa-security/scripts/:
| Script | Purpose |
|---|---|
sarif-parser.cjs | Parses SARIF v2.1.0 output |
json-parser.cjs | Parses Medusa JSON output |
finding-formatter.cjs | Formats findings with OWASP mapping |
main.cjs | Orchestrates the full pipeline |
cli-wrapper.cjs | Wraps Medusa CLI invocation |
security-review.cjs | Deterministic report writer (no Glob recursion) |
# Full scan with structured output
node .claude/skills/medusa-security/scripts/main.cjs --mode full --target .
# AI-only scan
node .claude/skills/medusa-security/scripts/main.cjs --mode ai-only --target .
# Quick scan (git-changed files)
node .claude/skills/medusa-security/scripts/main.cjs --mode quick --target .
Use this when you need the final security review report and want to avoid recursive Glob timeouts:
node .claude/skills/medusa-security/scripts/security-review.cjs
This writes:
/.claude/context/reports/security/security-review-medusa-scan-2026-02-17.md
and performs fixed-path checks on:
.claude/hooks/.claude/lib/.claude/skills/medusa-security/scripts/.claude/CLAUDE.md.claude/skills/medusa-security/**/* in long sessions.Findings are automatically mapped to:
| Severity | Action | Timeline |
|---|---|---|
| CRITICAL | Immediate fix | Before any merge |
| HIGH | Fix before release | Same sprint |
| MEDIUM | Fix in next sprint | Next cycle |
| LOW | Track and address | Backlog |
| Agent | Usage |
|---|---|
security-architect | Primary consumer. Use for comprehensive security reviews. |
penetration-tester | Use for targeted vulnerability scanning with authorization. |
code-reviewer | Use AI-only scan as part of code review workflow. |
# GitHub Actions example
- name: Security Scan
run: |
pip install medusa-security
medusa scan . --format sarif --fail-on high -o reports/
- name: Upload SARIF
uses: github/codeql-action/upload-sarif@v3
with:
sarif_file: reports/medusa-results.sarif
python -m medusa --version first; a missing install produces no output instead of an error, silently masking all vulnerabilities.--fail-on high in CI/CD pipelines — without a fail threshold, pipelines pass even when CRITICAL findings exist, creating false confidence in the security posture.github/codeql-action/upload-sarif@v3 persists findings for PR review, trend tracking, and compliance audit trails.| Anti-Pattern | Why It Fails | Correct Approach |
|---|---|---|
| Skipping installation check | Missing Medusa produces no output, not an error — all vulnerabilities silently missed | Run python -m medusa --version first; abort on non-zero exit |
| Using AI-only mode as a release gate | AI-only misses traditional SAST patterns (SQLi, XSS, path traversal) — 76 scanners needed for full coverage | Use full-scan mode for CI/CD gates; AI-only mode for rapid dev-time feedback only |
| No fail-on threshold in CI | Pipeline passes even when CRITICAL findings exist — false confidence in security posture | Always use --fail-on high in CI pipelines; adjust to --fail-on critical for high-risk repos |
| Ignoring MEDIUM findings | MEDIUM findings compound into exploitable chains when combined with HIGH findings | Triage MEDIUM findings each sprint; never allow them to accumulate without a tracking issue |
| Not uploading SARIF to Code Scanning | Findings live only in local files, lost after build — no PR-level review or trend tracking | Upload SARIF via github/codeql-action/upload-sarif@v3 in every CI run |
After scanning:
patterns.jsonissues.mdrecordGotcha() for recurring false positivesconst manager = require('.claude/lib/memory/memory-manager.cjs');
manager.recordGotcha({
text: 'False positive: medusa flags X pattern in Y context',
area: 'security-scanning',
});
manager.recordPattern({
text: 'Prompt injection found in CLAUDE.md context files',
area: 'ai-security',
});
security-architect — Threat modeling and OWASP analysisstatic-analysis — CodeQL and Semgrep SARIF analysissemgrep-rule-creator — Create custom Semgrep rulesinsecure-defaults — Detect hardcoded credentialsvariant-analysis — Discover vulnerability variants아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.