llm-security

Prevent prompt injection by applying structure-driven prompts, canary tokens, and explicit role separation. These controls establish strict boundaries to stop prompt hijacking and secure LLM outputs across your integration.

RAG security prevents context poisoning by applying provenance tagging, PII redaction, and tamper-evident context signing. It detects embedding poisoning to maintain integrity of retrieved context passed to generation models.

Secure MCP servers by enforcing strict input validation, tool-call schemas, and risk gates for high-impact actions. These controls validate tool invocations and prevent unauthorized actions within agent-based workflows.

Yes, you can use these guardrails for safety-critical AI systems in production. The skill provides governance controls, mTLS channels, and append-only audit trails to secure probabilistic AI components end-to-end.

Audit inter-agent messages for data leaks by implementing signed messages, mTLS channels, and append-only audit trails. This approach ensures tamper-evident logging and secure communication across multi-agent workflows.

Yes, you need specific tool-call schemas to validate inputs in LLM integrations. Strict schema validation acts as a risk gate to ensure only authorized, structured actions execute within agent-based workflows.