SHE把智能体安全从模型权重扩展到运行框架，分解成四个可独立进化的组件，实测攻击成功率降3.1倍，还能跨模型迁移，搞AI安全的值得看看。
SHE框架将智能体安全机制分解为系统提示、规则库、安全记忆和工具策略四个组件，每个组件有明确的安全职责。该框架通过归因引导的进化循环，将轨迹失败转化为结构化诊断，并学习组件特定的边界优化。在Agent-SafetyBench上，SHE相比静态SafeHarness将攻击成功率降低3.1倍，同时提升良性效用。进化后的安全机制在AgentHarm基准上泛化到未见风险，并无需额外进化即可跨智能体模型迁移。
SHE: Trajectory-driven Safety Harness Evolution for LLM Agents
The safety of large language model (LLM) agents depends not only on model weights but also on the agent harness that manages context, memory, tools, permissions, and runtime control. Existing safety mechanisms often treat the harness as a fixed deployment artifact, limiting their ability to evolve with emerging risks. Moreover, coupled functions across harness components obscure safety responsibility attribution, making localized evolution difficult. We propose Safety Harness Evolution (SHE), a framework that learns evolving safe boundaries from rollout trajectories. SHE decomposes the harness into four artifacts with explicit safety responsibilities, including the System Prompt, Rule Bank, Safety Memory, and Tool Policy, defining clear functional boundaries for localized evolution. Based on this decomposition, SHE introduces an attribution-guided evolution loop that converts trajectory failures into structured diagnoses, learns artifact-specific boundary refinements, and selects evolved harnesses through safety-utility validation. Experiments on Agent-SafetyBench demonstrate that SHE effectively enhances safety through harness evolution, achieving a 3.1x ASR reduction compared with static SafeHarness, while also improving benign utility. The evolved harness further generalizes to unseen risks on the held-out AgentHarm benchmark and transfers across agent models without additional evolution.