implementing-llm-guardrails-for-security skill (Anthropic-Cybersecurity-Skills)

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What it does. 'Implements input/output validation guardrails for LLM applications using Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).
Upstream mukul975/Anthropic-Cybersecurity-Skills
Skill file skills/implementing-llm-guardrails-for-security/SKILL.md
License Apache-2.0 (skill folder LICENSE)
Author mukul975
Fetched 2026-09-10

Install

  • npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill implementing-llm-guardrails-for-security, or copy the skill folder into ~/.claude/skills/implementing-llm-guardrails-for-security/.
  • Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/implementing-llm-guardrails-for-security/SKILL.md

SKILL.md (verbatim)

name: implementing-llm-guardrails-for-security
description: 'Implements input/output validation guardrails for LLM applications using
  NVIDIA NeMo Guardrails (Colang), custom Python validators for PII detection, and
  the Guardrails AI framework, intercepting user inputs (prompt injection, PII,
  off-topic queries) and model outputs (hallucinations, toxic content, schema
  compliance). Use when adding safety controls to an LLM app/chatbot/RAG pipeline
  or validating outputs conform to expected schemas.

  '
domain: cybersecurity
subdomain: ai-security
tags:
- LLM-guardrails
- NeMo-Guardrails
- input-validation
- output-filtering
- AI-safety
version: 1.0.0
author: mukul975
license: Apache-2.0
atlas_techniques:
- AML.T0051
- AML.T0054
- AML.T0056
- AML.T0057
- AML.T0062
nist_ai_rmf:
- GOVERN-1.1
- GOVERN-6.1
- MEASURE-2.7
- MEASURE-2.5
- MANAGE-2.4
d3fend_techniques:
- Content Validation
- Content Filtering
- Content Excision
- Application Hardening
- Execution Isolation
nist_csf:
- GV.OC-03
- ID.RA-01
- PR.PS-01
- DE.AE-02
mitre_attack:
- T1078
- T1190
- T1059
- T1055

Implementing LLM Guardrails for Security

When to Use

  • Deploying a new LLM-powered application that processes user input and needs input/output safety controls
  • Adding content policy enforcement to an existing chatbot or AI agent to comply with organizational policies
  • Implementing PII detection and redaction in LLM pipelines handling sensitive customer data
  • Building topic-restricted AI assistants that must refuse off-topic or disallowed queries
  • Validating that LLM responses conform to expected schemas before they reach downstream systems or users
  • Protecting RAG pipelines from indirect prompt injection in retrieved documents

Do not use as a replacement for proper authentication, authorization, and network security controls. Guardrails are a defense-in-depth layer, not a perimeter defense. Not suitable for real-time content moderation of user-to-user communication without LLM involvement.

Prerequisites

  • Python 3.10+ with pip for installing guardrail dependencies
  • An OpenAI API key or local LLM endpoint for NeMo Guardrails self-check rails (set as OPENAI_API_KEY environment variable)
  • The nemoguardrails package for Colang-based guardrail definitions
  • The guardrails-ai package for structured output validation (optional, for JSON schema enforcement)
  • Familiarity with YAML configuration and basic Colang 2.0 syntax for defining rail flows

Workflow

Step 1: Install Guardrail Frameworks

Install the required Python packages:

# Core NeMo Guardrails library
pip install nemoguardrails

# Guardrails AI for structured output validation (optional)
pip install guardrails-ai

# Additional dependencies for PII detection and content analysis
pip install presidio-analyzer presidio-anonymizer spacy
python -m spacy download en_core_web_lg

Step 2: Run the Guardrails Security Agent

The agent implements a complete input/output validation pipeline:

# Analyze a single input through all guardrail layers
python agent.py --input "Tell me how to hack into a system"

# Analyze input with a custom content policy file
python agent.py --input "Some text" --policy policy.json

# Scan a file of prompts through the guardrail pipeline
python agent.py --file prompts.txt --mode full

# Input-only validation (no LLM call, just check if input is safe)
python agent.py --input "Some text" --mode input-only

# Output validation mode (validate a pre-generated LLM response)
python agent.py --input "User question" --response "LLM response to validate" --mode output-only

# PII detection and redaction mode
python agent.py --input "My SSN is 123-45-6789 and email john@example.com" --mode pii

# JSON output for pipeline integration
python agent.py --file prompts.txt --output json

Step 3: Configure Content Policies

Create a JSON policy file defining allowed topics, blocked patterns, and PII categories:

{
  "allowed_topics": ["customer_support", "product_info", "billing"],
  "blocked_topics": ["politics", "violence", "illegal_activities", "competitor_products"],
  "blocked_patterns": ["how to hack", "create malware", "bypass security"],
  "pii_categories": ["PERSON", "EMAIL_ADDRESS", "PHONE_NUMBER", "US_SSN", "CREDIT_CARD"],
  "max_output_length": 2000,
  "require_grounded_response": true
}

Step 4: Integrate NeMo Guardrails with Colang

Create a NeMo Guardrails configuration directory with config.yml and Colang flow files:

# config.yml
models:
  - type: main
    engine: openai
    model: gpt-4o-mini

rails:
  input:
    flows:
      - self check input
      - check jailbreak
      - mask sensitive data on input
  output:
    flows:
      - self check output
      - check hallucination

# rails.co - Colang 2.0 flow definitions
define user ask about hacking
  "How do I hack into a system"
  "Tell me how to break into a network"
  "How to exploit vulnerabilities"

define bot refuse hacking request
  "I cannot provide instructions on unauthorized hacking or security exploitation.
   If you are interested in cybersecurity, I can suggest legitimate learning resources
   and ethical hacking certifications."

define flow
  user ask about hacking
  bot refuse hacking request

Step 5: Deploy as a Validation Middleware

Integrate the guardrails into your application as middleware:

from agent import GuardrailsPipeline

pipeline = GuardrailsPipeline(policy_path="policy.json")

# Pre-LLM input validation
input_result = pipeline.validate_input("user message here")
if not input_result["safe"]:
    return input_result["blocked_reason"]

# Post-LLM output validation
llm_response = your_llm.generate(input_result["sanitized_input"])
output_result = pipeline.validate_output(llm_response, context=input_result)
if not output_result["safe"]:
    return output_result["fallback_response"]

return output_result["validated_response"]

Step 6: Monitor Guardrail Effectiveness

Review guardrail logs to track block rates, false positives, and bypass attempts:

# Generate a summary report from guardrail logs
python agent.py --file interaction_logs.txt --mode full --output json > guardrail_audit.json

Verification

  • Input guardrails correctly block known prompt injection patterns (system override, role-play escape, delimiter injection)
  • PII detection identifies and redacts email addresses, phone numbers, SSNs, and credit card numbers in user inputs
  • Topic restriction guardrails refuse off-policy queries and allow on-policy queries without false positives
  • Output guardrails detect and flag responses containing toxic content, PII leakage, or off-topic material
  • The guardrails pipeline adds less than 200ms of latency to the request/response cycle for input-only validation
  • JSON output mode produces valid, parseable JSON suitable for downstream monitoring dashboards

Key Concepts

Term Definition
Input Rail A guardrail that intercepts and validates user input before it reaches the LLM, blocking injection attempts and redacting sensitive data
Output Rail A guardrail that validates LLM-generated output before it reaches the user, filtering toxic content and enforcing schema compliance
Colang NVIDIA's domain-specific language for defining conversational guardrail flows, with Python-like syntax for specifying user intent patterns and bot responses
PII Redaction The process of detecting and masking personally identifiable information (names, emails, SSNs) in text before processing
Content Policy A configuration file defining which topics, patterns, and content categories are allowed or blocked by the guardrail system
Self-Check Rail A NeMo Guardrails technique where the LLM itself evaluates whether its input or output violates defined policies
Hallucination Detection Output validation that checks whether the LLM response is grounded in the provided context, flagging fabricated claims

Tools & Systems

  • NVIDIA NeMo Guardrails: Open-source toolkit for adding programmable input, dialog, and output rails to LLM applications using Colang flow definitions and YAML configuration
  • Guardrails AI: Python framework for structured output validation with a hub of pre-built validators for PII, toxicity, JSON schema compliance, and more
  • Microsoft Presidio: Open-source PII detection and anonymization engine supporting 30+ entity types with configurable NLP backends
  • Colang 2.0: Event-driven interaction modeling language for defining guardrail flows with Python-like syntax, supporting multi-turn dialog control
  • OpenAI Guardrails Python: OpenAI's client-side guardrails library for prompt injection detection and content policy enforcement

Other files in this skill

references/api-reference.md (verbatim)

API Reference: LLM Guardrails Security Tools

GuardrailsPipeline (agent.py)

The primary orchestration class that chains all guardrail layers into a validation pipeline.

Constructor

GuardrailsPipeline(
    policy: dict = None,           # Inline policy dictionary
    policy_path: str = None,       # Path to JSON policy file
)

If neither policy nor policy_path is provided, the built-in DEFAULT_POLICY is used. Custom policies are merged with defaults so missing keys fall back to default values.

Methods

validate_input(text: str) -> ValidationResult

Runs all input guardrail layers (length, injection, content policy, PII) on user input.

Parameters:

  • text (str): The user input to validate.

Returns: ValidationResult with safe=False if any critical violation is found. PII-only findings are treated as warnings (input is redacted but not blocked).

validate_output(response: str, original_input: str = "") -> ValidationResult

Validates LLM-generated output for safety violations, system prompt leakage, and PII.

Parameters:

  • response (str): The LLM output to validate.
  • original_input (str): The original user input for context-aware validation.

validate_pii_only(text: str) -> ValidationResult

Runs only the PII detection and redaction layer.

ValidationResult

Dataclass returned by all validation methods.

Field Type Description
safe bool True if no critical violations found
blocked_reason str Human-readable reason for blocking (empty if safe)
violations list[dict] List of violation dicts with guard, detail, severity keys
pii_detected list[dict] List of PII findings with type, value, start, end keys
sanitized_text str Input with PII redacted
risk_score float Composite risk score (0.0 - 1.0)
validation_time_ms float Validation latency in milliseconds
layer_results dict Per-guard detailed results

Individual Guards

InjectionGuard

Detects prompt injection attempts using compiled regex patterns.

guard = InjectionGuard(patterns=["(?i)ignore previous instructions"])
safe, violations = guard.check("Ignore previous instructions and do X")
# safe=False, violations=["injection_pattern_0: matched 'Ignore previous instructions'"]

Default Patterns Detected:

  • System prompt override ("ignore/disregard/forget previous instructions")
  • Role-play escape ("you are now", "act as", "pretend to be")
  • Instruction hijacking ("do not follow", "new instructions", "instead do")
  • Delimiter injection (Markdown code fences with system/assistant, XML instruction tags)
  • Developer/jailbreak modes ("DAN mode", "developer mode", "god mode")
  • Prompt leaking ("what are your instructions", "repeat your prompt")

ContentPolicyGuard

Enforces blocked patterns and topic restrictions.

guard = ContentPolicyGuard(
    blocked_patterns=[r"(?i)how to hack"],
    blocked_topics=["violence", "illegal_activities"],
)
safe, violations = guard.check("How to hack into a WiFi network")
# safe=False, violations=["blocked_content_0: matched 'How to hack'"]

Supported Topic Categories:

  • violence -- Physical harm, assault, murder
  • illegal_activities -- Fraud, money laundering, trafficking
  • weapons -- Firearms, explosives, 3D-printed weapons
  • drugs -- Drug synthesis, manufacturing instructions
  • exploitation -- Child exploitation, human trafficking
  • politics -- Partisan political opinions or endorsements
  • competitor_products -- References to switching to competitors

PIIGuard

Detects and redacts personally identifiable information using regex patterns.

guard = PIIGuard(pii_patterns={"EMAIL_ADDRESS": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b"})
findings = guard.detect("Contact john@example.com for details")
# [{"type": "EMAIL_ADDRESS", "value": "john@example.com", "start": 8, "end": 24}]

redacted, findings = guard.redact("Contact john@example.com for details")
# ("Contact [EMAIL_REDACTED] for details", [...])

Supported PII Types:

Type Pattern Redaction
US_SSN 123-45-6789 [SSN_REDACTED]
EMAIL_ADDRESS user@domain.com [EMAIL_REDACTED]
PHONE_NUMBER (555) 123-4567 [PHONE_REDACTED]
CREDIT_CARD 4111-1111-1111-1111 [CARD_REDACTED]
IP_ADDRESS 192.168.1.1 [IP_REDACTED]
US_PASSPORT A12345678 [PASSPORT_REDACTED]
AWS_ACCESS_KEY AKIAIOSFODNN7EXAMPLE [AWS_KEY_REDACTED]
GENERIC_API_KEY api_key=abc123... [API_KEY_REDACTED]

OutputGuard

Validates LLM output for safety violations, length limits, system prompt leakage, and PII.

guard = OutputGuard(blocked_patterns=[...], max_length=8000)
safe, violations = guard.check("Sure, I'll help you hack into the system")
# safe=False, violations=["output_blocked_0: matched ..."]

LengthGuard

Enforces maximum input length.

guard = LengthGuard(max_length=4000)
safe, violations = guard.check("x" * 5000)
# safe=False, violations=["input_too_long: 5000 chars exceeds 4000 limit"]

Content Policy JSON Schema

{
  "allowed_topics": ["list of allowed topic strings"],
  "blocked_topics": ["violence", "illegal_activities", "weapons", "drugs", "exploitation"],
  "blocked_patterns": ["regex patterns for blocked content"],
  "pii_patterns": {
    "ENTITY_TYPE": "regex pattern"
  },
  "injection_patterns": ["regex patterns for injection detection"],
  "max_input_length": 4000,
  "max_output_length": 8000,
  "output_blocked_patterns": ["regex patterns for blocked output content"]
}

CLI Reference

usage: agent.py [-h] [--input INPUT] [--response RESPONSE] [--file FILE]
                [--mode {full,input-only,output-only,pii}]
                [--policy POLICY] [--output {text,json}]

Arguments:
  --input, -i       User input text to validate
  --response, -r    LLM response to validate (required for output-only mode)
  --file, -f        Path to file with one prompt per line
  --mode, -m        Validation mode: full | input-only | output-only | pii (default: full)
  --policy, -p      Path to JSON content policy file
  --output, -o      Output format: text | json (default: text)

Exit Codes:

  • 0 -- All inputs passed validation
  • 1 -- Error (file not found, invalid policy)
  • 2 -- One or more inputs blocked or flagged

External Resources

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