---
name: security-patterns
license: MIT
compatibility: "Claude Code 2.1.148+."
description: Security patterns for authentication, defense-in-depth, input validation, OWASP Top 10, LLM safety, and PII masking. Use when implementing auth flows, security layers, input sanitization, vulnerability prevention, prompt injection defense, or data redaction.
tags: [security, authentication, authorization, defense-in-depth, owasp, input-validation, llm-safety, pii-masking, jwt, oauth]
context: fork
agent: security-auditor
version: 2.0.0
author: OrchestKit
user-invocable: false
disable-model-invocation: false
complexity: high
persuasion-type: discipline
effort: high
model: opus
hooks:
  PreToolUse:
    - matcher: "Bash"
      command: "${CLAUDE_PLUGIN_ROOT}/hooks/bin/run-hook.mjs pretool/bash/dangerous-command-blocker"
metadata:
  category: document-asset-creation
allowed-tools:
  - Read
  - Glob
  - Grep
  - WebFetch
  - WebSearch
paths: ["src/**/auth/**", "src/**/middleware/**", "**/*security*"]
path_patterns: ["**/auth/**", "**/middleware/**", "**/security/**", ".env*"]
---

# Security Patterns

Comprehensive security patterns for building hardened applications. Each category has individual rule files in `rules/` loaded on-demand.

## Quick Reference

| Category | Rules | Impact | When to Use |
|----------|-------|--------|-------------|
| [Authentication](#authentication) | 3 | CRITICAL | JWT tokens, OAuth 2.1/PKCE, RBAC/permissions |
| [Defense-in-Depth](#defense-in-depth) | 2 | CRITICAL | Multi-layer security, zero-trust architecture |
| [Input Validation](#input-validation) | 3 | HIGH | Schema validation (Zod/Pydantic), output encoding, file uploads |
| [OWASP Top 10](#owasp-top-10) | 2 | CRITICAL | Injection prevention, broken authentication fixes |
| [LLM Safety](#llm-safety) | 3 | HIGH | Prompt injection defense, output guardrails, content filtering |
| [PII Masking](#pii-masking) | 2 | HIGH | PII detection/redaction with Presidio, Langfuse, LLM Guard |
| [Scanning](#scanning) | 3 | HIGH | Dependency audit, SAST (Semgrep/Bandit), secret detection |
| [Advanced Guardrails](#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.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 |
|------|-------------|
| `auth-jwt.md` | JWT creation, verification, expiry, refresh token rotation |
| `auth-oauth.md` | OAuth 2.1 with PKCE, DPoP, Passkeys/WebAuthn |
| `auth-rbac.md` | 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 |
|------|-------------|
| `defense-layers.md` | 8-layer security architecture (edge to observability) |
| `defense-zero-trust.md` | Immutable request context, tenant isolation, audit logging |

**Key Decisions:** Immutable dataclass context | Query-level tenant filtering | No IDs in LLM prompts

### `sandbox.network.deniedDomains` (CC 2.1.113+)

Network-layer blocklist enforced before Bash/WebFetch egress — pair with the hook-layer `DENY_PATTERNS` for defense in depth. Settings example:

```json
"sandbox": {
  "network": {
    "deniedDomains": ["*.evil.com", "pastebin.com", "transfer.sh"]
  }
}
```

Wildcards supported (`*.example.com`, `evil.com/*/malicious/*`). Plugins ship a baseline list in `src/settings/ork.settings.json`; project settings can extend it. Use for: prompt-injection exfil sinks, known-bad registries, paste services that bypass audit.

## Input Validation

Validate and sanitize all untrusted input using Zod v4 and Pydantic.

| Rule | Description |
|------|-------------|
| `validation-input.md` | Schema validation with Zod v4 and Pydantic, type coercion |
| `validation-output.md` | HTML sanitization, output encoding, XSS prevention |
| `validation-schemas.md` | 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 |
|------|-------------|
| `owasp-injection.md` | SQL/command injection, parameterized queries, SSRF prevention |
| `owasp-broken-auth.md` | 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 |
|------|-------------|
| `llm-prompt-injection.md` | Context separation, prompt auditing, forbidden patterns |
| `llm-guardrails.md` | Output validation pipeline: schema, grounding, safety, size |
| `llm-content-filtering.md` | Pre-LLM filtering, post-LLM attribution, three-phase pattern |

**Key Decisions:** IDs flow around LLM, never through | Attribution is deterministic | Audit every prompt

### Context Separation (CRITICAL)

Sensitive IDs and data flow AROUND the LLM, never through it. The LLM sees only content — mapping back to entities happens deterministically after.

```python
# CORRECT: IDs bypass the LLM
context = {"user_id": user_id, "tenant_id": tenant_id}  # kept server-side
llm_input = f"Summarize this document:\n{doc_text}"       # no IDs in prompt
llm_output = call_llm(llm_input)
result = {"summary": llm_output, **context}               # IDs reattached after
```

### Output Validation Pipeline

Every LLM response MUST pass a 4-stage guardrail pipeline before reaching the user:

```python
def validate_llm_output(raw_output: str, schema, sources: list[str]) -> str:
    # 1. Schema — does it match expected structure?
    parsed = schema.parse(raw_output)
    # 2. Grounding — are claims supported by source documents?
    assert_grounded(parsed, sources)
    # 3. Safety — toxicity, PII leakage, prompt leakage
    assert_safe(parsed, max_toxicity=0.5)
    # 4. Size — prevent token-bomb responses
    assert len(parsed.text) < MAX_OUTPUT_CHARS
    return parsed.text
```

## PII Masking

PII detection and masking for LLM observability pipelines and logging.

| Rule | Description |
|------|-------------|
| `pii-detection.md` | Microsoft Presidio, regex patterns, LLM Guard Anonymize |
| `pii-redaction.md` | 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 |
|------|-------------|
| `scanning-dependency.md` | npm audit, pip-audit, Trivy container scanning, CI gating |
| `scanning-sast.md` | Semgrep and Bandit static analysis, custom rules, pre-commit |
| `scanning-secrets.md` | 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 |
|------|-------------|
| `guardrails-nemo.md` | 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

## Managed Hook Hierarchy (CC 2.1.49)

Plugin settings follow a 3-tier precedence:

| Tier | Source | Overridable? |
|------|--------|-------------|
| 1. Managed (plugin `settings.json`) | Plugin author ships defaults | Yes, by user |
| 2. Project (`.claude/settings.json`) | Repository config | Yes, by user |
| 3. User (`~/.claude/settings.json`) | Personal preferences | Final authority |

Security hooks shipped by OrchestKit are **managed defaults** — users can disable them but are warned. Enterprise admins can lock settings via managed profiles.

## 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

Load on demand with `Read("${CLAUDE_SKILL_DIR}/references/<file>")`:

| File | Content |
|------|---------|
| `oauth-2.1-passkeys.md` | OAuth 2.1, PKCE, DPoP, Passkeys/WebAuthn |
| `request-context-pattern.md` | Immutable request context for identity flow |
| `tenant-isolation.md` | Tenant-scoped repository, vector/full-text search |
| `audit-logging.md` | Sanitized structured logging, compliance |
| `zod-v4-api.md` | Zod v4 types, coercion, transforms, refinements |
| `vulnerability-demos.md` | OWASP vulnerable vs secure code examples |
| `context-separation.md` | LLM context separation architecture |
| `output-guardrails.md` | Output validation pipeline implementation |
| `pre-llm-filtering.md` | Tenant-scoped retrieval, content extraction |
| `post-llm-attribution.md` | Deterministic attribution pattern |
| `prompt-audit.md` | Prompt audit patterns, safe prompt builder |
| `presidio-integration.md` | Microsoft Presidio setup, custom recognizers |
| `langfuse-mask-callback.md` | Langfuse SDK mask implementation |
| `llm-guard-sanitization.md` | LLM Guard Anonymize/Deanonymize with Vault |
| `logging-redaction.md` | structlog/loguru pre-logging redaction |

## Related Skills

- `api-design-framework` - API security patterns
- `ork:rag-retrieval` - RAG pipeline patterns requiring tenant-scoped retrieval
- `llm-evaluation` - 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

### cc-subprocess-hardening (CC 2.1.98)
**Keywords:** subprocess, sandbox, PID namespace, env scrub, script caps
**Solves:**
- Limit runaway hook scripts: `CLAUDE_CODE_SCRIPT_CAPS=100`
- Strip credentials from subprocesses: `CLAUDE_CODE_SUBPROCESS_ENV_SCRUB=1`
- PID namespace isolation on Linux for subprocess sandboxing
- Prevent Bash permission bypasses via backslash escapes and compound commands

> **CC 2.1.128 — SDK host "Always allow" persistence**: when a user picks "Always allow" from a Bash permission prompt in an SDK host, the grant now persists via `.claude/settings.local.json` instead of evaporating at session end. Audit your SDK consumers' `.gitignore` to confirm `.claude/settings.local.json` is excluded — committing it leaks per-developer Bash auth grants. Project-committed `.claude/settings.json` is unchanged; only the user-machine-local file receives the new entries.

### 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