ai-safety-guardrails

What problem does it solve?

This Skill solves critical security and safety gaps in production LLM applications, protecting against prompt injection, sensitive data exfiltration, harmful content generation, and insecure tool execution that can lead to data breaches, compliance violations, and system compromise.

Core Features & Use Cases

• Multi-Layer Input/Output Guardrails: Enforce input length limits, detect known jailbreak patterns, run content moderation on user inputs, and validate LLM outputs against schemas, check for hallucinations, and moderate harmful content before delivery to users.
• Prompt Injection Defense: Use structural patterns like delimiter isolation and sandwich defense to prevent user input from hijacking system instructions, with specialized protection for RAG pipelines where retrieved documents may carry adversarial content.
• Tool and Data Safety: Implement least-privilege tool access, parameter validation to block path traversal and SQL injection, confirmation gates for irreversible actions, PII anonymization via Presidio, and full audit logging for all tool calls.
• Red-Teaming and Monitoring: Integrate with open-source safety classifiers like LlamaGuard and NeMo Guardrails, run automated red-team test suites in CI/CD, and set up sanitized logging for anomaly detection.
• Use Case Example: Use this Skill when building a customer support LLM chatbot that accesses internal order databases, to ensure malicious user inputs cannot inject commands to delete customer records or steal sensitive order data.

Quick Start

Use the ai-safety-guardrails skill to implement input validation, prompt injection defense, and output moderation for your new customer support LLM chatbot that accesses internal order databases.