Mend.io Publishes Guide for Securing AI Agents and LLM Applications

Mend.io has released a practitioner guide titled "Securing AI agents, MCP servers & LLM apps: A practical framework" addressing security challenges in deploying agentic AI systems to production. The guide prioritizes three core objectives: discovering deployed agents and Model Context Protocol servers, prioritizing security findings for faster remediation, and implementing runtime protections in production environments. Unlike traditional application security, agentic AI introduces novel attack surfaces including prompt injection through data, over-permissioned agent behavior, and tool poisoning via MCP server descriptions. The framework provides seven reusable artifacts, including an extended AI bill-of-materials template tracking nine fields per agent, a 12-point misconfiguration checklist, and guardrail deployment patterns supporting both Python SDKs and standalone Docker containers. Discovery methods include repository scanning for agentic signatures, network egress monitoring for model API calls, and continuous automation to prevent configuration drift.