Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
日本語の概要は準備中です。原文の説明を表示しています。
Scans for security vulnerabilities including XSS, SQL injection, CSRF, and auth flaws using OWASP Top 10 methodology. Use when conducting SAST/DAST scans, auditing authentication flows, testing authorization rules, or implementing security test automation.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
<default_to_action> When testing security or conducting audits:
Quick Security Checks:
Critical Success Factors:
| # | Vulnerability | Key Test |
|---|---|---|
| 1 | Broken Access Control | User A accessing User B's data |
| 2 | Cryptographic Failures | Plaintext passwords, HTTP |
| 3 | Injection | SQL/XSS/command injection |
| 4 | Insecure Design | Rate limiting, session timeout |
| 5 | Security Misconfiguration | Verbose errors, exposed /admin |
| 6 | Vulnerable Components | npm audit, outdated packages |
| 7 | Auth Failures | Weak passwords, no MFA |
| 8 | Integrity Failures | Unsigned updates, malware |
| 9 | Logging Failures | No audit trail for breaches |
| 10 | SSRF | Server fetching internal URLs |
| Type | Tool | Purpose |
|---|---|---|
| SAST | SonarQube, Semgrep | Static code analysis |
| DAST | OWASP ZAP, Burp | Dynamic scanning |
| Deps | npm audit, Snyk | Dependency vulnerabilities |
| Secrets | git-secrets, TruffleHog | Secret scanning |
qe-security-scanner: Multi-layer SAST/DAST scanningqe-api-contract-validator: API security testingqe-quality-analyzer: Security code review// Horizontal escalation - User A accessing User B's data
test('user cannot access another user\'s order', async () => {
const userAToken = await login('userA');
const userBOrder = await createOrder('userB');
const response = await api.get(`/orders/${userBOrder.id}`, {
headers: { Authorization: `Bearer ${userAToken}` }
});
expect(response.status).toBe(403);
});
// Vertical escalation - Regular user accessing admin
test('regular user cannot access admin', async () => {
const userToken = await login('regularUser');
expect((await api.get('/admin/users', {
headers: { Authorization: `Bearer ${userToken}` }
})).status).toBe(403);
});
// SQL Injection
test('prevents SQL injection', async () => {
const malicious = "' OR '1'='1";
const response = await api.get(`/products?search=${malicious}`);
expect(response.body.length).toBeLessThan(100); // Not all products
});
// XSS
test('sanitizes HTML output', async () => {
const xss = '<script>alert("XSS")</script>';
await api.post('/comments', { text: xss });
const html = (await api.get('/comments')).body;
expect(html).toContain('<script>');
expect(html).not.toContain('<script>');
});
test('passwords are hashed', async () => {
await db.users.create({ email: 'test@example.com', password: 'MyPassword123' });
const user = await db.users.findByEmail('test@example.com');
expect(user.password).not.toBe('MyPassword123');
expect(user.password).toMatch(/^\$2[aby]\$\d{2}\$/); // bcrypt
});
test('no sensitive data in API response', async () => {
const response = await api.get('/users/me');
expect(response.body).not.toHaveProperty('password');
expect(response.body).not.toHaveProperty('ssn');
});
test('errors don\'t leak sensitive info', async () => {
const response = await api.post('/login', { email: 'nonexistent@test.com', password: 'wrong' });
expect(response.body.error).toBe('Invalid credentials'); // Generic message
});
test('sensitive endpoints not exposed', async () => {
const endpoints = ['/debug', '/.env', '/.git', '/admin'];
for (let ep of endpoints) {
expect((await fetch(`https://example.com${ep}`)).status).not.toBe(200);
}
});
test('rate limiting prevents brute force', async () => {
const responses = [];
for (let i = 0; i < 20; i++) {
responses.push(await api.post('/login', { email: 'test@example.com', password: 'wrong' }));
}
expect(responses.filter(r => r.status === 429).length).toBeGreaterThan(0);
});
# GitHub Actions
security-checks:
steps:
- name: Dependency audit
run: npm audit --audit-level=high
- name: SAST scan
run: npm run sast
- name: Secret scan
uses: trufflesecurity/trufflehog@main
- name: DAST scan
if: github.ref == 'refs/heads/main'
run: docker run owasp/zap2docker-stable zap-baseline.py -t https://staging.example.com
Pre-commit hooks:
#!/bin/sh
git-secrets --scan
npm run lint:security
// Comprehensive multi-layer scan
await Task("Security Scan", {
target: 'src/',
layers: { sast: true, dast: true, dependencies: true, secrets: true },
severity: ['critical', 'high', 'medium']
}, "qe-security-scanner");
// OWASP Top 10 testing
await Task("OWASP Scan", {
categories: ['broken-access-control', 'injection', 'cryptographic-failures'],
depth: 'comprehensive'
}, "qe-security-scanner");
// Validate fix
await Task("Validate Fix", {
vulnerability: 'CVE-2024-12345',
expectedResolution: 'upgrade package to v2.0.0',
retestAfterFix: true
}, "qe-security-scanner");
aqe/security/
├── scans/* - Scan results
├── vulnerabilities/* - Found vulnerabilities
├── fixes/* - Remediation tracking
└── compliance/* - Compliance status
const securityFleet = await FleetManager.coordinate({
strategy: 'security-testing',
agents: [
'qe-security-scanner',
'qe-api-contract-validator',
'qe-quality-analyzer',
'qe-deployment-readiness'
],
topology: 'parallel'
});
Hiding admin at /super-secret-admin → Use proper auth
JavaScript validation can be bypassed → Always validate server-side
Assuming input is safe → Sanitize, validate, escape all input
API keys in code → Environment variables, secret management
For v3 agent-specific commands (aqe security ...), SAST/DAST scanning code, compliance audits (SOC2/GDPR/HIPAA), secret detection, and security gates, see references/compliance-agent-commands.md.
Think like an attacker: What would you try to break? Test that. Build like a defender: Assume input is malicious until proven otherwise. Test continuously: Security testing is ongoing, not one-time.
With Agents: Agents automate vulnerability scanning, track remediation, and validate fixes. Use agents to maintain security posture at scale.
After each security scan, append results to run-history.json in this skill directory:
node -e "
const fs = require('fs');
const h = JSON.parse(fs.readFileSync('.claude/skills/security-testing/run-history.json'));
h.runs.push({date: new Date().toISOString().split('T')[0], scan_types: ['sast','deps'], findings: {critical: 0, high: 0, medium: 0, low: 0}});
fs.writeFileSync('.claude/skills/security-testing/run-history.json', JSON.stringify(h, null, 2));
"
Read run-history.json before each scan — track finding count by severity over time. Alert if critical findings increase.
/code-review-quality for combined quality + security review/pentest-validation to prove exploitability/compliance-testing for regulatory requirementsnpm audit may report false positives for dev dependencies — filter with --omit=dev for production-relevant resultsnpm audit --all catches nested vulnerabilitiesまだレビューはありません。使ってみた感想をお寄せください。
ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
まだコメントはありません。最初のコメントを書いてみませんか。
Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
日本語の概要は準備中です。原文の説明を表示しています。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
日本語の概要は準備中です。原文の説明を表示しています。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
日本語の概要は準備中です。原文の説明を表示しています。