Security Patterns
Comprehensive security patterns for building hardened applications. Each category has individual rule files in
loaded on-demand.
Quick Reference
| Category | Rules | Impact | When to Use |
|---|
| Authentication | 3 | CRITICAL | JWT tokens, OAuth 2.1/PKCE, RBAC/permissions |
| Defense-in-Depth | 2 | CRITICAL | Multi-layer security, zero-trust architecture |
| Input Validation | 3 | HIGH | Schema validation (Zod/Pydantic), output encoding, file uploads |
| OWASP Top 10 | 2 | CRITICAL | Injection prevention, broken authentication fixes |
| LLM Safety | 3 | HIGH | Prompt injection defense, output guardrails, content filtering |
| PII Masking | 2 | HIGH | PII detection/redaction with Presidio, Langfuse, LLM Guard |
| Scanning | 3 | HIGH | Dependency audit, SAST (Semgrep/Bandit), secret detection |
| Advanced Guardrails | 2 | CRITICAL | NeMo/Guardrails AI validators, red-teaming, OWASP LLM |
Total: 20 rules across 8 categories
Quick Start
python
# Argon2id password hashing
from argon2 import PasswordHasher
ph = PasswordHasher()
password_hash = ph.hash(password)
ph.verify(password_hash, password)
python
# JWT access token (15-min expiry)
import jwt
from datetime import datetime, timedelta, timezone
payload = {
'sub': user_id, 'type': 'access',
'exp': datetime.now(timezone.utc) + timedelta(minutes=15),
}
token = jwt.encode(payload, SECRET_KEY, algorithm='HS256')
typescript
// Zod v4 schema validation
import { z } from 'zod';
const UserSchema = z.object({
email: z.string().email(),
name: z.string().min(2).max(100),
role: z.enum(['user', 'admin']).default('user'),
});
const result = UserSchema.safeParse(req.body);
python
# PII masking with Langfuse
import re
from langfuse import Langfuse
def mask_pii(data, **kwargs):
if isinstance(data, str):
data = re.sub(r'\b[\w.-]+@[\w.-]+\.\w+\b', '[REDACTED_EMAIL]', data)
data = re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '[REDACTED_SSN]', data)
return data
langfuse = Langfuse(mask=mask_pii)
Authentication
Secure authentication with OAuth 2.1, Passkeys/WebAuthn, JWT tokens, and role-based access control.
| Rule | Description |
|---|
| JWT creation, verification, expiry, refresh token rotation |
| OAuth 2.1 with PKCE, DPoP, Passkeys/WebAuthn |
| Role-based access control, permission decorators, MFA |
Key Decisions: Argon2id > bcrypt | Access tokens 15 min | PKCE required | Passkeys > TOTP > SMS
Defense-in-Depth
Multi-layer security architecture with no single point of failure.
| Rule | Description |
|---|
| 8-layer security architecture (edge to observability) |
| Immutable request context, tenant isolation, audit logging |
Key Decisions: Immutable dataclass context | Query-level tenant filtering | No IDs in LLM prompts
Input Validation
Validate and sanitize all untrusted input using Zod v4 and Pydantic.
| Rule | Description |
|---|
| Schema validation with Zod v4 and Pydantic, type coercion |
| HTML sanitization, output encoding, XSS prevention |
| Discriminated unions, file upload validation, URL allowlists |
Key Decisions: Allowlist over blocklist | Server-side always | Validate magic bytes not extensions
OWASP Top 10
Protection against the most critical web application security risks.
| Rule | Description |
|---|
| SQL/command injection, parameterized queries, SSRF prevention |
| JWT algorithm confusion, CSRF protection, timing attacks |
Key Decisions: Parameterized queries only | Hardcode JWT algorithm | SameSite=Strict cookies
LLM Safety
Security patterns for LLM integrations including context separation and output validation.
| Rule | Description |
|---|
| Context separation, prompt auditing, forbidden patterns |
| Output validation pipeline: schema, grounding, safety, size |
| Pre-LLM filtering, post-LLM attribution, three-phase pattern |
Key Decisions: IDs flow around LLM, never through | Attribution is deterministic | Audit every prompt
PII Masking
PII detection and masking for LLM observability pipelines and logging.
| Rule | Description |
|---|
| Microsoft Presidio, regex patterns, LLM Guard Anonymize |
| Langfuse mask callback, structlog/loguru processors, Vault deanonymization |
Key Decisions: Presidio for enterprise | Replace with type tokens | Use mask callback at init
Scanning
Automated security scanning for dependencies, code, and secrets.
| Rule | Description |
|---|
| npm audit, pip-audit, Trivy container scanning, CI gating |
| Semgrep and Bandit static analysis, custom rules, pre-commit |
| Gitleaks, TruffleHog, detect-secrets with baseline management |
Key Decisions: Pre-commit hooks for shift-left | Block on critical/high | Gitleaks + detect-secrets baseline
Advanced Guardrails
Production LLM safety with NeMo Guardrails, Guardrails AI validators, and DeepTeam red-teaming.
| Rule | Description |
|---|
| NeMo Guardrails, Colang 2.0 flows, Guardrails AI validators, layered validation |
guardrails-llm-validation.md
| DeepTeam red-teaming (40+ vulnerabilities), OWASP LLM Top 10 compliance |
Key Decisions: NeMo for flows, Guardrails AI for validators | Toxicity 0.5 threshold | Red-team pre-release + quarterly
Anti-Patterns (FORBIDDEN)
python
# Authentication
user.password = request.form['password'] # Plaintext password storage
response_type=token # Implicit OAuth grant (deprecated)
return "Email not found" # Information disclosure
# Input Validation
"SELECT * FROM users WHERE name = '" + name + "'" # SQL injection
if (file.type === 'image/png') {...} # Trusting Content-Type header
# LLM Safety
prompt = f"Analyze for user {user_id}" # ID in prompt
artifact.user_id = llm_output["user_id"] # Trusting LLM-generated IDs
# PII
logger.info(f"User email: {user.email}") # Raw PII in logs
langfuse.trace(input=raw_prompt) # Unmasked observability data
Detailed Documentation
| Resource | Description |
|---|
| references/oauth-2.1-passkeys.md | OAuth 2.1, PKCE, DPoP, Passkeys/WebAuthn |
| references/request-context-pattern.md | Immutable request context for identity flow |
| references/tenant-isolation.md | Tenant-scoped repository, vector/full-text search |
| references/audit-logging.md | Sanitized structured logging, compliance |
| references/zod-v4-api.md | Zod v4 types, coercion, transforms, refinements |
| references/vulnerability-demos.md | OWASP vulnerable vs secure code examples |
| references/context-separation.md | LLM context separation architecture |
| references/output-guardrails.md | Output validation pipeline implementation |
| references/pre-llm-filtering.md | Tenant-scoped retrieval, content extraction |
| references/post-llm-attribution.md | Deterministic attribution pattern |
| references/prompt-audit.md | Prompt audit patterns, safe prompt builder |
| references/presidio-integration.md | Microsoft Presidio setup, custom recognizers |
| references/langfuse-mask-callback.md | Langfuse SDK mask implementation |
| references/llm-guard-sanitization.md | LLM Guard Anonymize/Deanonymize with Vault |
| references/logging-redaction.md | structlog/loguru pre-logging redaction |
Related Skills
- - API security patterns
- - RAG pipeline patterns requiring tenant-scoped retrieval
- - Output quality assessment including hallucination detection
Capability Details
authentication
Keywords: password, hashing, JWT, token, OAuth, PKCE, passkey, WebAuthn, RBAC, session
Solves:
- Implement secure authentication with modern standards
- JWT token management with proper expiry
- OAuth 2.1 with PKCE flow
- Passkeys/WebAuthn registration and login
- Role-based access control
defense-in-depth
Keywords: defense in depth, security layers, multi-layer, request context, tenant isolation
Solves:
- How to secure AI applications end-to-end
- Implement 8-layer security architecture
- Create immutable request context
- Ensure tenant isolation at query level
input-validation
Keywords: schema, validate, Zod, Pydantic, sanitize, HTML, XSS, file upload
Solves:
- Validate input against schemas (Zod v4, Pydantic)
- Prevent injection attacks with allowlists
- Sanitize HTML and prevent XSS
- Validate file uploads by magic bytes
owasp-top-10
Keywords: OWASP, sql injection, broken access control, CSRF, XSS, SSRF
Solves:
- Fix OWASP Top 10 vulnerabilities
- Prevent SQL and command injection
- Implement CSRF protection
- Fix broken authentication
llm-safety
Keywords: prompt injection, context separation, guardrails, hallucination, LLM output
Solves:
- Prevent prompt injection attacks
- Implement context separation (IDs around LLM)
- Validate LLM output with guardrail pipeline
- Deterministic post-LLM attribution
pii-masking
Keywords: PII, masking, Presidio, Langfuse, redact, GDPR, privacy
Solves:
- Detect and mask PII in LLM pipelines
- Integrate masking with Langfuse observability
- Implement pre-logging redaction
- GDPR-compliant data handling