Part 3: AI Agent Security Vulnerabilities - Brain and Perception Module Analysis
In Part 1 of this series, we explored how AI agents are transforming enterprise technology with their ability to perceive, decide, and act autonomously.
In Part 2, we examined three critical shifts in AI system evolution that have fundamentally altered the security landscape: the transition from rules-based to learning-based systems, the progression from single-task to multi-task capabilities, and the advancement from tool-using to tool-creating agents.
Today, we'll take a technical deep dive into the anatomy of modern AI agents, examining what's happening under the hood and the specific security vulnerabilities in each core component. As organizations rapidly adopt these powerful systems, understanding these vulnerabilities becomes essential for security professionals tasked with protecting their environments.
At its core, an AI agent consists of three primary components: the Brain (typically an LLM) that handles reasoning and decision-making, the Perception module that processes environmental inputs, and the Action module that interacts with systems and tools. Each component introduces unique security challenges that, when combined, create a complex attack surface unlike anything we've seen in traditional systems.