AI Agent Security Audit: From MCP Penetration Testing to LLM Vulnerability Assessment
The rapid adoption of AI agents and MCP (Model Context Protocol) servers has introduced a new attack surface that traditional security tools were never designed to cover. Over the past 90 days, our research team has conducted systematic AI security audit across 10 major AI frameworks — including CrewAI, AutoGen, LlamaIndex, LangGraph, Dify, and Haystack — uncovering 24 distinct vulnerability patterns that affect production LLM systems.
This article shares our methodology, key findings, and practical recommendations for teams running LLM vulnerability assessment programs.
The New Attack Surface: Why AI Agents Are Different
Traditional web application security focuses on injection, broken authentication, and misconfiguration. AI agents introduce three fundamentally new risk categories:
