How to Secure AI Agents, MCP Servers, and LLM Apps in Production

A new practical guide covers the key security attack surfaces introduced by production AI agent deployments, with specific focus on Model Context Protocol servers and LLM-backed applications. The piece addresses prompt injection, tool misuse, credential leakage through context windows, and unauthorized action execution — all of which become critical once agents can call external APIs or interact with file systems. For developers shipping agentic systems, this is an essential checklist covering both design-time mitigations and runtime monitoring strategies. MCP in particular is highlighted as a new and under-secured layer, as its rapid adoption has outpaced security guidance. Teams deploying agents in enterprise contexts should treat this as a baseline review before production launch.