LLM / AI Defensive Security Reference

Defensive practitioner reference for hardening LLM-backed applications and agents. Every section is framed as defense, hardening, detection, or verification — it describes the misconfiguration, how to detect it, and how to prevent it, never how to exploit it. Cross-domain web-app vulnerabilities live in moai-ref-owasp-checklist; API design lives in moai-ref-api-patterns.

Target Use

Apply when reviewing or building an LLM-backed system — a chat product, a retrieval-augmented application, an autonomous agent, or an MCP server. The material assumes an untrusted-input threat model: any text the model reads (user turns, retrieved documents, tool results, file contents) may carry adversarial instructions, and any text the model emits may be acted on downstream.

Trust Boundaries in an LLM System