How to Secure AI Agents, MCP Servers, and LLM Apps in Production
Agents, MCP integrations, and LLM-powered applications are entering codebases faster than most security programs can track them. Mend.io’s new practitioner guide, ‘Securing AI agents, MCP servers & LLM apps: A practical framework’, targets that gap. It is organized around three moves: see what matters, fix what matters faster, protect AI in production and ships seven reusable artifacts. Why traditional AppSec breaks AppSec was built on one assumption: applications do what their
Get smart on it
A practitioner guide addresses how to secure AI agents, MCP servers, and large language model applications in production environments. Traditional application security approaches fail for agentic AI because agent behavior emerges from multiple sources—models, prompts, context, user input, and available tools—rather than code alone, creating new failure modes like prompt injection through data and over-permissioned agents taking harmful actions without exploited vulnerabilities. The guide proposes a framework organized around three moves: discovering and mapping what matters through a five-layer attack surface, prioritizing and triaging findings with evidence-backed automation, and protecting systems at runtime through guardrails, prompt hardening, and monitoring. The approach aligns to established security frameworks including NIST AI RMF, OWASP AIMA, ISO/IEC 42001, and the EU AI Act.
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