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

Researchers introduced the Agent-Native Immune System (ANIS), a biologically inspired defense architecture embedded within autonomous agents' cognitive loops to counter runtime attacks like memory poisoning and tool-chain manipulation. The framework includes a six-layer Immune Tower, a taxonomy of Agent Viruses and Vaccines, and a Harness Triad for continual immune learning, distinguishing it from static model alignment. ANIS aims to address vulnerabilities in current external defenses as agents evolve from chatbots to autonomous systems.

arXiv:2606.28270v1 Announce Type: new Abstract: The transition from static chat bots to autonomous agents--equipped with persistent memory, tool-use protocols, and multi-agent collaboration--has fundamentally expanded the AI threat landscape. Current defense mechanisms, such as perimeter security and training-time alignment, remain external to the agent's active reasoning loop. Consequently, they fall short: a fully aligned agent remains highly vulnerable to runtime hijacking via memory poisoning, tool-chain manipulation, or multi-agent protocol attacks. To address this critical gap, we introduce the Agent-Native Immune System ANIS , the first biologically inspired, endogenous defense architecture embedded directly within the agent's cognitive loop. Our framework presents four primary contributions. First, we design a six-layer Immune Tower L0-L5 , distinctly incorporating Barrier Immunity L1 as a non-cognitive, physical-and-logical isolation layer. Second, we establish a unified taxonomy of Agent Viruses and Agent Vaccines, formalizing the critical distinction between superficial non-parametric defenses and robust parametric vaccines. Third, we conceptualize the Harness Triad--Meta, Self, and Auto--a self-monitoring, meta-cognitive automation backbone that drives Continual Immune Learning CIL , enabling vaccines to dynamically adapt to novel threats. Finally, we establish a rigorous theoretical demarcation between model alignment and agent immunity: while alignment provides a static "constitutional" value foundation during training, ANIS serves as the dynamic "law enforcement" mechanism during runtime. We conclude by framing open challenges for the field, including immune protocol standardization, novel evaluation metrics such as the Autoimmunity Rate false-positive intervention rate , and the co-evolutionary dynamics between pathogens and vaccines within collective intelligence ecosystems.