Security Scanner

Security Scanner

Proactive pre-deployment security assessment. SAST pattern matching, secrets detection, dependency scanning, OWASP/CWE mapping, and compliance heuristics.

Scope: Pre-deployment security audit only. NOT for code review (use review), authorized live offensive testing (use pentest with signed ROE), penetration testing without static audit context, runtime security monitoring, or supply chain deep analysis.

Canonical Vocabulary

Dispatch

Empty / Help Gallery

When $ARGUMENTS is empty, show the dispatch table above plus examples:

• /security-scanner scan
• /security-scanner check src/auth/
• /security-scanner deps
• /security-scanner secrets
• /security-scanner compliance soc2
• /security-scanner report

State that a full scan runs only after the user picks a mode (for example scan); empty invocation is menu-only.

Mode: scan

Full codebase security assessment with triage and sampling for large codebases.

Step 1: Triage

1. Enumerate files: find or Glob to build file inventory
2. Risk-stratify files into HIGH/MEDIUM/LOW security relevance:
   • HIGH: auth, crypto, payments, user input handling, API endpoints, config with secrets
   • MEDIUM: data models, middleware, utilities touching external I/O
   • LOW: static assets, tests, documentation, pure computation
3. For 100+ files: sample — all HIGH, 50% MEDIUM, 10% LOW
4. Build dependency graph of HIGH-risk files

Step 2: SAST Pattern Scan

Read HIGH and sampled MEDIUM/LOW files. Match against patterns from references/owasp-patterns.md:

• Injection flaws (SQL, command, path traversal, template, LDAP)
• Authentication/session weaknesses
• Sensitive data exposure (logging PII, plaintext storage)
• XXE, SSRF, deserialization
• Security misconfiguration
• XSS (reflected, stored, DOM)
• Insecure direct object references
• Missing access controls
• CSRF vulnerabilities
• Using components with known vulnerabilities

Step 3: Secrets Scan

Run: uv run python scripts/secrets-detector.py <path> Parse JSON output. Cross-reference findings with .gitignore coverage.

Step 4: Dependency Check

If lockfiles exist, run: uv run python scripts/dependency-checker.py <path> Parse JSON output. Flag outdated or unmaintained dependencies.

Step 5: CWE/OWASP Mapping

Map each finding to CWE IDs and OWASP Top 10 categories using references/cwe-patterns.md. Assign severity (CRITICAL/HIGH/MEDIUM/LOW/INFO) and confidence (0.0-1.0).

Step 6: Remediation

For each finding with confidence >= 0.7, provide:

• CWE reference link
• Specific remediation guidance
• Code example when applicable

Step 7: Report

Present findings grouped by severity. Include:

• Executive summary with finding counts by severity
• Detailed findings with CWE, OWASP, evidence, remediation
• Dependency health summary (if lockfiles scanned)
• Secrets summary (count by type, no values exposed)

Mode: check

Targeted security check on specific files or directories.

1. Read the specified file(s)
2. Apply full SAST pattern matching (no triage/sampling — scan everything)
3. Run secrets detection on the path
4. Map findings to CWE/OWASP
5. Present findings with remediation

Mode: deps

Dependency lockfile analysis.

1. Detect lockfiles: package-lock.json, yarn.lock, pnpm-lock.yaml, requirements.txt, uv.lock, Cargo.lock, go.sum, Gemfile.lock, composer.lock
2. Run: uv run python scripts/dependency-checker.py <path>
3. Parse output: dependency names, versions, ecosystem
4. Flag: outdated packages, packages with known CVE patterns, unusual version pinning
5. Present dependency health report

Mode: secrets

Secrets-only scan using regex patterns.

1. Run: uv run python scripts/secrets-detector.py <path>
2. Parse JSON findings
3. Cross-reference with .gitignore — flag secrets in tracked files as CRITICAL
4. Check git history for previously committed secrets: git log --diff-filter=D -p -- <file>
5. Present findings grouped by secret type, never exposing actual values

Mode: compliance

Lightweight compliance heuristic scoring.

1. Validate <standard> is one of: soc2, gdpr, hipaa
2. Run: uv run python scripts/compliance-scorer.py <path> --standard <standard>
3. Read reference checklist from references/compliance-checklists.md
4. Score each control as PASS/FAIL/PARTIAL with evidence
5. Present compliance scorecard with overall percentage and failing controls

Mode: report

Generate visual security dashboard.

1. Collect all findings from the current session (or re-run scan if none exist)
2. Format findings as JSON matching the dashboard schema
3. Convert to SARIF if requested: uv run python scripts/sarif-formatter.py
4. Inject JSON into templates/dashboard.html
5. Copy to a temporary file, open in browser

Scaling Strategy

Reference Files

Load ONE reference at a time. Do not preload all references into context.

Critical Rules

1. Never expose actual secret values in output — show type, file, line only
2. Every finding must map to at least one CWE ID
3. Confidence < 0.3 = discard; 0.3-0.7 = flag as potential; >= 0.7 = report
4. Run secrets-detector.py before reporting — regex patterns catch what LLM scanning misses
5. Do not report phantom vulnerabilities requiring impossible conditions
6. For 100+ files, always triage before scanning — never brute-force the full codebase
7. Dependency findings require version evidence — never flag without checking the actual version
8. Compliance mode is heuristic only — state this explicitly in output, never claim certification
9. Present findings before suggesting fixes — always use an approval gate
10. Cross-reference with .gitignore — secrets in untracked files are INFO, in tracked files are CRITICAL
11. Load ONE reference file at a time — do not preload all references into context
12. This skill is for pre-deployment audit only — redirect to review for code review, refuse pen testing requests
13. SARIF output must conform to SARIF v2.1 schema — validate with sarif-formatter.py
14. Never modify source files — this skill is read-only analysis