--- name: implementing-llm-guardrails-for-security-e72d132720a7 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: ```bash # 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: ```bash # 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: ```json { "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: ```yaml # 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 ``` ```colang # 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: ```python 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: ```bash # 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 --- Source attribution: hironow Original project: https://github.com/hironow/cyber/blob/20efb3a6a2a9e7ebbde38ed32325979866110540/.agents/skills/implementing-llm-guardrails-for-security/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Dave AI. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Dave AI resource metadata Creator / publisher: hironow Original project: https://github.com/hironow/cyber/blob/20efb3a6a2a9e7ebbde38ed32325979866110540/.agents/skills/implementing-llm-guardrails-for-security/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering Roles: Data Scientist, ML / AI Engineer, Security Engineer Specialties: API, Backend Tools: Guardrails Ai, Nemoguardrails, Presidio Analyzer, Presidio Anonymizer, Spacy Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 0afc4da2525e21f3f6f4fb01055e7dfb1daffb11b98ce298a6b1e8d024836fe5