// Production-grade security testing with agentic vulnerability detection, SAST/DAST tools, OWASP Top 10 coverage, threat modeling, and AI-powered security analysis achieving 92% detection accuracy (OpenAI Aardvark benchmark 2024)
Build ultra-low-latency edge computing applications with Cloudflare Workers, Workers AI for LLM inference, V8 isolates, Durable Objects, and serverless patterns deployed across 330+ data centers worldwide
Production-grade FastAPI development with async patterns, Pydantic v2, dependency injection, ML/AI endpoint design, and modern Python best practices for building high-performance REST APIs
Production-grade observability stack with Prometheus metrics, Grafana dashboards, PromQL query language, alerting rules, and AI-powered anomaly detection for modern cloud-native applications
Production-grade PostgreSQL query optimization, indexing strategies, performance tuning, and modern features including pgvector for AI/ML workloads. Master EXPLAIN plans, query analysis, and database design for high-performance applications
Production-grade Terraform development with HCL best practices, module design, state management, multi-cloud patterns, and AI-enhanced infrastructure as code for scalable cloud deployments
Production-grade Web3 development with Solidity smart contracts, ethers.js/web3.js integration, DApp architecture, security patterns, and AI-enhanced blockchain development (ChatWeb3, Aider + Gemini 2024)
| name | Security & Vulnerability Testing |
| skill_id | security-testing |
| version | 1.0.0 |
| description | Production-grade security testing with agentic vulnerability detection, SAST/DAST tools, OWASP Top 10 coverage, threat modeling, and AI-powered security analysis achieving 92% detection accuracy (OpenAI Aardvark benchmark 2024) |
| category | Security & Testing |
| tags | ["security","vulnerability-testing","penetration-testing","owasp","sast","dast","threat-modeling","security-automation","ai-security","code-review"] |
| author | mcp-skillset |
| license | MIT |
| created | "2025-11-25T00:00:00.000Z" |
| last_updated | "2025-11-25T00:00:00.000Z" |
| toolchain | ["OWASP ZAP","Snyk","Bandit (Python)","GitGuardian","Checkov (IaC)"] |
| frameworks | ["OWASP Testing Framework","STRIDE Threat Modeling","MITRE ATT&CK"] |
| related_skills | ["test-driven-development","systematic-debugging","fastapi-web-development","terraform-infrastructure"] |
Master security testing with AI-powered vulnerability detection, automated SAST/DAST scanning, and threat modeling. OpenAI's Aardvark achieves 92% vulnerability detection accuracy (2024), and multi-agent security tools find 4x more vulnerabilities than traditional scanners.
# A01:2021 – Broken Access Control
# ❌ BAD: Missing authorization check
@app.get("/api/users/{user_id}/profile")
async def get_profile(user_id: int):
return await db.get(User, user_id) # Anyone can access any profile!
# ✅ GOOD: Verify user authorization
@app.get("/api/users/{user_id}/profile")
async def get_profile(user_id: int, current_user: User = Depends(get_current_user)):
if current_user.id != user_id and not current_user.is_admin:
raise HTTPException(403, "Forbidden")
return await db.get(User, user_id)
# A02:2021 – Cryptographic Failures
# ❌ BAD: Weak password hashing
import hashlib
password_hash = hashlib.md5(password.encode()).hexdigest() # NEVER USE MD5!
# ✅ GOOD: Strong password hashing with bcrypt
from passlib.context import CryptContext
pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
password_hash = pwd_context.hash(password)
# A03:2021 – Injection
# ❌ BAD: SQL injection vulnerability
query = f"SELECT * FROM users WHERE email = '{email}'" # NEVER!
cursor.execute(query)
# ✅ GOOD: Parameterized queries
query = "SELECT * FROM users WHERE email = %s"
cursor.execute(query, (email,))
# Or use ORM (SQLAlchemy, Django ORM)
user = await db.execute(select(User).where(User.email == email))
# A04:2021 – Insecure Design
# ✅ Implement rate limiting
from slowapi import Limiter
limiter = Limiter(key_func=get_remote_address)
@app.post("/api/login")
@limiter.limit("5/minute")
async def login(credentials: LoginRequest):
# Prevent brute force attacks
pass
# A05:2021 – Security Misconfiguration
# ❌ BAD: Debug mode in production
app = FastAPI(debug=True) # NEVER in production!
# ✅ GOOD: Environment-aware configuration
app = FastAPI(debug=settings.DEBUG)
# A06:2021 – Vulnerable and Outdated Components
# ✅ Run: pip-audit, snyk test, npm audit
# A07:2021 – Identification and Authentication Failures
# ✅ Use OAuth2 with JWT, implement MFA
# A08:2021 – Software and Data Integrity Failures
# ✅ Sign releases, verify checksums, use SRI for CDN resources
# A09:2021 – Security Logging and Monitoring Failures
# ✅ Log all authentication attempts, failed access, sensitive operations
# A10:2021 – Server-Side Request Forgery (SSRF)
# ❌ BAD: Unchecked URL fetching
url = request.json.get("url")
response = await httpx.get(url) # Can access internal services!
# ✅ GOOD: Whitelist allowed domains
ALLOWED_DOMAINS = ["api.example.com", "cdn.example.com"]
def is_safe_url(url: str) -> bool:
parsed = urllib.parse.urlparse(url)
return parsed.netloc in ALLOWED_DOMAINS
if not is_safe_url(url):
raise HTTPException(400, "Invalid URL")
# Python: Bandit
pip install bandit
bandit -r ./app -f json -o security-report.json
# Example issues Bandit detects:
# - Hardcoded passwords
# - Use of eval() or exec()
# - SQL injection risks
# - Weak cryptography
# JavaScript/TypeScript: ESLint Security Plugin
npm install --save-dev eslint-plugin-security
# Add to .eslintrc.json:
# "plugins": ["security"]
# Infrastructure: Checkov (Terraform, CloudFormation, Kubernetes)
pip install checkov
checkov -d ./terraform --output json
# Secrets: GitGuardian or detect-secrets
pip install detect-secrets
detect-secrets scan --baseline .secrets.baseline
# Multi-language: Snyk
npm install -g snyk
snyk test
snyk code test # SAST
snyk container test myapp:latest # Container scanning
SAST Integration in CI/CD:
# GitHub Actions
- name: Run Bandit Security Scan
run: |
pip install bandit
bandit -r . -f json -o bandit-report.json
continue-on-error: true
- name: Upload Security Report
uses: actions/upload-artifact@v3
with:
name: security-reports
path: bandit-report.json
# OWASP ZAP (Zed Attack Proxy)
docker run -t owasp/zap2docker-stable zap-baseline.py \
-t https://example.com \
-r zap-report.html
# Nikto (web server scanner)
nikto -h https://example.com -o nikto-report.txt
# Nuclei (fast vulnerability scanner)
nuclei -u https://example.com -t ~/nuclei-templates/
# Burp Suite (commercial, GUI-based)
# Manual testing and automated scanning
DAST Best Practices:
STRIDE Framework (Microsoft):
S - Spoofing Identity
✅ Use multi-factor authentication
✅ Implement certificate pinning
❌ Never trust client-provided identity
T - Tampering with Data
✅ Use HTTPS/TLS for all communication
✅ Implement request signing (HMAC)
❌ Never accept unsigned data from clients
R - Repudiation
✅ Log all critical operations with timestamps
✅ Implement audit trails
❌ Never allow users to deny actions
I - Information Disclosure
✅ Encrypt sensitive data at rest and in transit
✅ Implement proper access controls
❌ Never expose internal error details
D - Denial of Service
✅ Implement rate limiting and throttling
✅ Use CDN and DDoS protection (Cloudflare)
❌ Never allow unbounded resource consumption
E - Elevation of Privilege
✅ Follow principle of least privilege
✅ Validate permissions at every access point
❌ Never trust user roles from client
Threat Modeling Process:
# ❌ BAD: Secrets in code
API_KEY = "sk-1234567890abcdef" # NEVER!
DATABASE_URL = "postgresql://user:pass@host/db"
# ❌ BAD: Secrets in version control
# .env file committed to git
# ✅ GOOD: Environment variables (12-factor app)
import os
API_KEY = os.getenv("API_KEY")
DATABASE_URL = os.getenv("DATABASE_URL")
# ✅ GOOD: Secret management services
# AWS Secrets Manager
import boto3
client = boto3.client('secretsmanager')
secret = client.get_secret_value(SecretId='prod/api-key')
API_KEY = secret['SecretString']
# HashiCorp Vault
import hvac
client = hvac.Client(url='http://vault:8200', token=vault_token)
secret = client.secrets.kv.v2.read_secret_version(path='api-key')
API_KEY = secret['data']['data']['key']
# Kubernetes Secrets
# Mounted as files or environment variables
Secret Scanning:
# GitGuardian (detects 500+ secret types)
pip install ggshield
ggshield secret scan path .
# TruffleHog (git history scanning)
docker run -it trufflesecurity/trufflehog:latest github --repo https://github.com/myorg/myrepo
# Gitleaks
gitleaks detect --source . --report-path gitleaks-report.json
Agentic Security Testing Workflow:
1. Code Analysis
- Read codebase and understand behavior
- Identify potential vulnerability surfaces
2. Test Generation
- Generate test cases targeting vulnerabilities
- Create exploit attempts
3. Test Execution
- Run tests in sandbox environment
- Observe application behavior
4. Reasoning Loop
- Analyze results
- Generate new hypotheses
- Iterate until vulnerability confirmed or ruled out
Benchmark: 92% detection rate on synthetic vulnerabilities
Agent Roles:
1. Architecture Mapper: Build system model
2. Threat Modeler: Apply STRIDE framework
3. Code Reviewer: Static analysis with AI reasoning
4. Penetration Tester: Generate and execute exploits
Result: 16-17 vulnerabilities found (4x vs single-agent)
# ❌ Vulnerable
query = f"SELECT * FROM users WHERE id = {user_id}"
db.execute(query)
# ✅ Fixed
query = "SELECT * FROM users WHERE id = %s"
db.execute(query, (user_id,))
// ❌ Vulnerable
document.innerHTML = userInput;
// ✅ Fixed
document.textContent = userInput;
// Or use a sanitization library
import DOMPurify from 'dompurify';
document.innerHTML = DOMPurify.sanitize(userInput);
# ❌ Vulnerable
@app.get("/files/{file_id}")
async def download(file_id: int):
return FileResponse(f"/uploads/{file_id}.pdf")
# ✅ Fixed
@app.get("/files/{file_id}")
async def download(file_id: int, user: User = Depends(get_current_user)):
file = await db.get(File, file_id)
if file.owner_id != user.id:
raise HTTPException(403, "Forbidden")
return FileResponse(file.path)
# ❌ Vulnerable
import xml.etree.ElementTree as ET
tree = ET.parse(user_uploaded_xml)
# ✅ Fixed
from defusedxml.ElementTree import parse
tree = parse(user_uploaded_xml)
# 1. Reconnaissance
nmap -sV -sC target.com
whatweb target.com
whois target.com
# 2. Vulnerability Scanning
nuclei -u https://target.com -t ~/nuclei-templates/
nikto -h https://target.com
# 3. Exploitation (ethical, authorized only!)
# Use Metasploit, Burp Suite, custom scripts
# 4. Post-Exploitation
# Document findings, severity, remediation
# 5. Reporting
# Create detailed report with PoC, impact, fixes
# Security-focused code review checklist:
# 1. Authentication
# - Are passwords hashed properly?
# - Is rate limiting implemented?
# - Are sessions managed securely?
# 2. Authorization
# - Is every endpoint protected?
# - Are permissions checked correctly?
# - Can users access other users' data?
# 3. Input Validation
# - Are all inputs validated?
# - Is SQL injection prevented?
# - Is XSS prevented?
# 4. Output Encoding
# - Is user data sanitized before display?
# - Are error messages generic?
# 5. Cryptography
# - Are modern algorithms used (AES-256, RSA-2048+)?
# - Are keys stored securely?
# - Is HTTPS enforced?
# 6. Error Handling
# - Are exceptions caught and logged?
# - Do error messages leak information?
# 7. Logging
# - Are security events logged?
# - Are logs protected from tampering?