Audit Security

Single-Session Security Audit

When to Use

• Tasks related to audit-security
• User explicitly invokes /audit-security

When NOT to Use

• When the task doesn't match this skill's scope -- check related skills
• When a more specialized skill exists for the specific task

Execution Mode Selection

Section A: Parallel Architecture (4 Agents)

When to use: Task tool available, sufficient context budget, no S0/S1 in scope

Agent 1: vulnerability-scanner

Focus Areas:

• Authentication & Authorization
• Input Validation & Injection Prevention
• OWASP Top 10 Coverage

Files:

• app/api/**/*.ts (API routes)
• middleware.ts
• lib/auth*.ts
• Form handlers, input components

Agent 2: supply-chain-auditor

Focus Areas:

• Dependency Security & Supply Chain
• Package vulnerabilities (npm audit)
• License compliance
• Unpinned versions, risky postinstall scripts

Files:

• package.json, package-lock.json
• All import statements
• functions/package.json

Agent 3: framework-security-auditor

Focus Areas:

• Firebase/Firestore Security (rules, Cloud Functions)
• Next.js/Framework-Specific Security
• Hosting & Headers Security
• Server/client boundary leaks

Files:

• firestore.rules, storage.rules
• firebase.json
• functions/src/**/*.ts
• next.config.mjs

Agent 4: ai-code-security-auditor

Focus Areas:

• AI-Generated Code & Agent Security
• Crypto & Randomness
• File Handling Security
• Product/UX Security Risks

Files:

• .claude/ configs
• Files with crypto, random, hash patterns
• File upload handlers
• Admin UI components

Parallel Execution Command

Invoke all 4 agents in a SINGLE Task message:

Task 1: vulnerability-scanner agent - audit auth, input validation, OWASP Task
2: supply-chain-auditor agent - audit dependencies, npm packages Task 3:
framework-security-auditor agent - audit Firebase, Next.js, headers Task 4:
ai-code-security-auditor agent - audit AI patterns, crypto, files

Each agent prompt MUST end with:

CRITICAL RETURN PROTOCOL:

- Write findings to the specified output file using Write tool or Bash
- Return ONLY: `COMPLETE: [agent-id] wrote N findings to [output-path]`
- Do NOT return full findings content — orchestrator checks completion via file

Dependency constraints: All 4 agents are independent -- no ordering required. Each writes to a separate JSONL section. S0/S1 findings trigger immediate notification but do not block other agents.

Coordination Rules

1. Each agent writes findings to separate JSONL section
2. S0/S1 findings trigger immediate notification
3. File conflicts resolved by framework-security-auditor (highest authority)
4. All agents respect rate-limiting and App Check patterns

Section B: Sequential Fallback (Single Agent)

When to use: Task tool unavailable, context limits, or user preference

Execution Order (priority-first):

1. AI Security Patterns (S0 potential) - 15 min
2. Auth & Authorization - 10 min
3. Input Validation - 10 min
4. Supply Chain (npm audit) - 5 min
5. Remaining categories - 20 min

Total: ~60 min (vs ~15 min parallel)

Checkpointing: After each category, write intermediate findings to file before continuing. This protects against context loss.

Checkpoint Format

{
  "started_at": "ISO timestamp",
  "categories_completed": ["Auth", "Input"],
  "current_category": "DataProtection",
  "findings_count": 24,
  "last_file_written": "stage-2-findings.jsonl"
}

Write Checkpoint After Each Category

• Update ${AUDIT_DIR}/checkpoint.json
• Append findings to .jsonl file (not overwrite)
• This enables recovery regardless of agent capability

Pre-Audit Validation

Step 0: Episodic Memory Search (Session #128)

Before running security audit, search for context from past security sessions:

// Search for past security audit findings
mcp__plugin_episodic -
  memory_episodic -
  memory__search({
    query: ["security audit", "S0", "vulnerability"],
    limit: 5,
  });

// Search for specific vulnerability patterns addressed before
mcp__plugin_episodic -
  memory_episodic -
  memory__search({
    query: ["OWASP", "injection", "auth bypass"],
    limit: 5,
  });

Why this matters:

• Compare against previous security findings (regression detection)
• Identify recurring vulnerabilities (may indicate architectural issue)
• Track which S0/S1 issues were resolved vs still open
• Prevent re-flagging known false positives

Step 1: Check Thresholds

Run npm run review:check and report results. Check for security-sensitive file changes.

• Display count of security-sensitive files changed
• If none: "⚠️ No security-sensitive changes detected. Proceed anyway?"
• Continue with audit regardless (user invoked intentionally)

Step 2: Gather Current Baselines

Collect these metrics by running commands:

# Dependency vulnerabilities (extract summary without truncating JSON)
npm audit --json 2>/dev/null | node -e '
try {
  const d = JSON.parse(require("fs").readFileSync(0,"utf8"));
  console.log(JSON.stringify(d.metadata?.vulnerabilities ?? d.vulnerabilities ?? {}, null, 2));
} catch (e) {
  console.log("{\"error\": \"Invalid JSON from npm audit\"}");
}
'

# Security lint warnings
npm run lint 2>&1 | grep -i "security" | head -10

# Pattern compliance (security patterns)
npm run patterns:check 2>&1

# Check for .env files (existence only - no permission/owner metadata needed)
ls .env* 2>/dev/null || echo "No .env files found"

Step 2b: Query SonarCloud Security (if MCP available)

If mcp__sonarcloud__get_security_hotspots is available:

• Query with status: "TO_REVIEW" to get unresolved security hotspots
• Note hotspot count and severity distribution

If mcp__sonarcloud__get_issues is available:

• Query with types: "VULNERABILITY" to get security-specific issues
• Cross-reference with npm audit findings for comprehensive coverage

This provides real-time security issue data from static analysis.

Step 3: Load False Positives Database

Read docs/technical-debt/FALSE_POSITIVES.jsonl and filter findings matching:

• Category: security
• Expired entries (skip if expires date passed)

Note patterns to exclude from final findings.

Step 4: Check Template Currency

Read docs/audits/multi-ai/templates/SECURITY_AUDIT_PLAN.md and verify:

• FIREBASE_CHANGE_POLICY.md reference is valid
• Security-sensitive file list is current
• OWASP categories are complete
• Firestore rules path is correct

If outdated, note discrepancies but proceed with current values.

Audit Execution

Focus Areas (12 Categories):

1. Authentication & Authorization (auth checks, role validation, IDOR, privilege escalation)

2. Input Validation & Injection Prevention:

   • SQL/NoSQL injection, command injection
   • Template injection, eval/Function(), new Function()
   • Unsafe deserialization, prototype pollution

3. Data Protection (encryption, PII handling, secrets, overly verbose errors)

4. Firebase/Firestore Security (rules, Cloud Functions, rate limiting, replay protection)

5. Dependency Security & Supply Chain:

   • npm audit, outdated packages
   • Unpinned versions, risky postinstall scripts
   • Unused dependencies with known vulnerabilities

6. OWASP Top 10 Coverage

7. Hosting & Headers Security:

   • CSP, HSTS, X-Frame-Options, X-Content-Type-Options
   • COOP, COEP, Referrer-Policy, Permissions-Policy

8. Next.js/Framework-Specific:

   • Server/client boundary leaks (secrets in client bundles)
   • API route / middleware auth gates
   • Static export vs server rendering assumptions

9. File Handling Security:

   • Insecure file upload, path traversal
   • MIME type validation, file size limits

10. Crypto & Randomness:

    • Weak randomness (Math.random for security), broken hashing
    • Unsafe JWT/session handling, homegrown crypto

11. Product/UX Security Risks:

    • Misleading "security UI" (toggles without server enforcement)
    • Dangerous defaults not clearly communicated
    • Admin-only flows accessible via client routes without backend checks

12. AI-Generated Code & Agent Security:

    • Prompt-injection surfaces in scripts/configs
    • Agent config files with unsafe patterns
    • Suspicious strings/comments that could manipulate AI agents

13. AI Security Patterns (AI-Codebase Specific):

    • Prompt Injection Surfaces: .claude/ configs, agent prompts, LLM integrations with user input (S0)
    • Hallucinated Security APIs: Security functions that don't exist or have wrong signatures (S1)
    • AI-Suggested Insecure Defaults: Default any types, * CORS, overly permissive rules (S1)
    • Inconsistent Auth Patterns: Different auth approaches across AI sessions (S2)
    • Over-Confident Comments: "This is secure because..." without actual security (S2)
    • AI-Generated Crypto: Homegrown crypto patterns AI might generate (S0)

For each category:

1. Search relevant files using Grep/Glob
2. Identify specific vulnerabilities with file:line references
3. Classify severity: S0 (Critical) | S1 (High) | S2 (Medium) | S3 (Low)
4. Classify OWASP category if applicable
5. Estimate effort: E0 (trivial) | E1 (hours) | E2 (day) | E3 (major)
6. Assign confidence level (see Evidence Requirements below)

Security-Sensitive Files to Check:

• firestore.rules, storage.rules
• functions/src/**/*.ts
• lib/firebase*.ts, lib/auth*.ts
• middleware.ts, next.config.mjs
• firebase.json (hosting headers, rewrites)
• .env* files (environment variables)
• package.json, package-lock.json (supply chain)
• .claude/ configs (agent security)
• Any file with "security", "auth", "token", "secret", "credential" in name

Additional Checks for Vibe-Coded Apps:

• Search for eval(, new Function(, Function( - dynamic code execution
• Search for dangerouslySetInnerHTML - XSS vectors
• Search for NEXT_PUBLIC_ env vars - ensure no secrets leaked
• Search for process.env in client components - boundary leaks
• Search for postinstall, preinstall in package.json - supply chain
• Search for suspicious patterns in .claude/ that could be prompt injection

Scope:

• Include: app/, components/, lib/, functions/, firestore.rules, firebase.json, .claude/
• Exclude: node_modules/, .next/, docs/, tests/

Output Requirements

1. Markdown Summary (display to user):

## Security Audit - [DATE]

### Baselines

- npm audit: X vulnerabilities (Y critical, Z high)
- Security patterns: X violations
- Security-sensitive files: X changed since last audit

### Findings Summary

| Severity | Count | OWASP Category | Confidence  |
| -------- | ----- | -------------- | ----------- |
| S0       | X     | ...            | HIGH/MEDIUM |
| S1       | X     | ...            | HIGH/MEDIUM |
| S2       | X     | ...            | ...         |
| S3       | X     | ...            | ...         |

### Critical/High Findings (Immediate Action)

1. [file:line] - Description (S0/OWASP-A01) - DUAL_PASS_CONFIRMED
2. ...

### False Positives Filtered

- X findings excluded (matched FALSE_POSITIVES.jsonl patterns)

### Dependency Vulnerabilities

- ...

### Recommendations

- ...

2. JSONL Findings (save to file):

Create file: docs/audits/single-session/security/audit-[YYYY-MM-DD].jsonl

Category field: category MUST be security. Also include owasp_category and cvss_estimate fields.

3. Markdown Report (save to file):

Create file: docs/audits/single-session/security/audit-[YYYY-MM-DD].md

Full markdown report with all findings, baselines, and remediation plan.

Standard Audit Procedures

Read .claude/skills/_shared/AUDIT_TEMPLATE.md for: Evidence Requirements, Dual-Pass Verification, Cross-Reference Validation, JSONL Output Format, Context Recovery, Post-Audit Validation, MASTER_DEBT Cross-Reference, Interactive Review, TDMS Intake & Commit, Documentation References, Agent Return Protocol, and Honesty Guardrails.

Skill-specific TDMS intake:

node scripts/debt/intake-audit.js <output.jsonl> --source "audit-security-<date>"

Security audit triggers (check AUDIT_TRACKER.md):

• ANY security-sensitive file modified, OR
• 20+ commits since last security audit

Version History

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