Agent-Native Immune System: Architecture, Taxonomy, and Engineering

Imagine you're building an autonomous agent that can interact with users and other agents, but you realize that these systems can be hijacked or manipulated in real-time. Current defenses, like setting up barriers before the agent acts, don't work well because they can't adapt once the agent is running. For instance, if someone poisons the agent's memory or tricks it into using the wrong tools, it can lead to serious vulnerabilities. This is what's called runtime hijacking and memory poisoning.

To tackle these issues, the authors propose a new approach called the Agent-Native Immune System (ANIS). Instead of relying on external defenses, ANIS embeds security directly into the agent's thinking process. It features a six-layer structure that includes a physical and logical isolation layer to protect the agent's core functions. Additionally, it categorizes threats and defenses into a unified system, allowing the agent to learn and adapt to new attacks continuously.

What sets ANIS apart from previous work is its focus on dynamic adaptation during runtime, contrasting with traditional static defenses. This means that while other systems might be well-prepared during training, ANIS can respond to new threats as they arise, making it a more resilient choice for developers building autonomous agents.

The paper introduces a novel defense architecture that embeds security directly into agent reasoning.

The claims are supported by a clear framework and theoretical distinctions, though empirical validation is less detailed.