security-patterns

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Quick Start

# Argon2id password hashing

from argon2 import PasswordHasher

ph = PasswordHasher()

password_hash = ph.hash(password)

ph.verify(password_hash, password)

# 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')

// 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);

# 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:

"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.

# 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:

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

1. Project (.claude/settings.json)

Repository config

Yes, by user

1. 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)

# 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