这篇论文把免疫系统思路直接嵌入到智能体内部，用六层防护对抗运行时攻击，和传统外围防御完全不同，值得看看。
这篇论文提出ANIS（Agent-Native Immune System），首个嵌入智能体认知循环的生物启发式内生防御架构。它设计了六层免疫塔（L0-L5），其中L1作为非认知的物理与逻辑隔离层。论文建立了智能体病毒和智能体疫苗的统一分类，并提出了Harness Triad（Meta、Self、Auto）实现持续免疫学习（CIL），使疫苗能动态适应新威胁。ANIS在运行时提供动态“执法”机制，与静态的模型对齐形成互补。
Agent-Native Immune System: Architecture, Taxonomy, and Engineering
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.