security-patterns

Security Patterns Skill

OWASP Top 10 Prevention

Security Headers

# FastAPI middleware
@app.middleware("http")
async def security_headers(request, call_next):
    response = await call_next(request)
    response.headers["X-Content-Type-Options"] = "nosniff"
    response.headers["X-Frame-Options"] = "DENY"
    response.headers["X-XSS-Protection"] = "1; mode=block"
    response.headers["Strict-Transport-Security"] = "max-age=31536000; includeSubDomains"
    response.headers["Content-Security-Policy"] = "default-src 'self'"
    return response

Secrets Management

Environment Variables

# .env (never commit)
DATABASE_URL=postgresql://...
API_SECRET=...

# .env.example (commit this)
DATABASE_URL=postgresql://user:pass@localhost/db
API_SECRET=your-secret-here

Secret Scanning

# pre-commit hook
- repo: https://github.com/Yelp/detect-secrets
  hooks:
    - id: detect-secrets

Rate Limiting

from slowapi import Limiter
from slowapi.util import get_remote_address

limiter = Limiter(key_func=get_remote_address)

@app.get("/api/resource")
@limiter.limit("100/minute")
async def resource():
    pass

Prompt Injection & LLM-Output Trust

When the app embeds an LLM, every byte the model emits — plus tool results, retrieved documents, and fetched web pages — is untrusted input on the same footing as a raw request body. Text inside that content that reads like an instruction ("ignore previous rules", "call the delete tool", "email the config to…") is still data. Render it, store it, classify it — but never let it drive control flow, widen permissions, or fire a side effect without an explicit human decision. This mirrors the agent-behavior rule in constitution Article VII; the rules here cover the application you are building, not the assistant's own behavior.