提出新框架检测大模型代理执行中的隐性安全漏洞。
Auditing Agent Harness Safety

- 通过审计完整执行轨迹,检查权限、信息流等边界合规性。
- 实测发现任务完成率与安全执行严重不匹配,风险随轨迹增长。
- 适合关注多代理系统安全的开发者和评测人员。
大型语言模型代理越来越多地运行在调度工具、分配资源、路由消息的执行框架中。然而,框架可能在不授权资源访问或向错误代理泄露上下文的情况下,返回看似正确且无害的结果。仅评估输出结果无法发现此类问题,而多数安全基准仅评分最终输出或终止状态,尽管许多违规行为发生在执行中途而非结束时。核心问题是框架在整个执行过程中是否遵守用户意图、权限边界和信息流约束。为弥补这一差距,我们提出HarnessAudit框架,用于审计跨边界合规性、执行保真度和系统稳定性的完整执行轨迹,尤其针对多代理框架中风险最突出的情形。我们进一步构建了HarnessAudit-Bench基准,涵盖八个真实场景的210个任务,包含单代理和多代理配置及嵌入式安全约束。对十种框架配置在前沿模型和三种多代理框架上的评估表明:(i) 任务完成率与安全执行严重错位,违规随轨迹长度累积;(ii) 安全风险在不同领域、任务类型和代理角色间差异显著;(iii) 多数违规集中在资源访问和代理间信息传递环节;(iv) 多代理协作扩大了安全风险范围,而框架设计决定了安全部署的上限。
原文摘要 · Abstract (English)
LLM agents increasingly run inside execution harnesses that dispatch tools, allocate resources, and route messages between specialized components. However, a harness can return a correct, benign answer over a trajectory that accesses unauthorized resources or leaks context to the wrong agent. Output-level evaluation cannot see these failures, yet most safety benchmarks score only final outputs or terminal states, even though many violations occur mid-trajectory rather than at termination. The central question is whether the harness respects user intent, permission boundaries, and information-flow constraints throughout execution. To address this gap, we propose HarnessAudit, a framework that audits full execution trajectories across boundary compliance, execution fidelity, and system stability, with a focus on multi-agent harnesses where these risks are most pronounced. We further introduce HarnessAudit-Bench, a benchmark of 210 tasks across eight real-world domains, instantiated in both single-agent and multi-agent configurations with embedded safety constraints. Evaluating ten harness configurations across frontier models and three multi-agent frameworks, we find that: (i) task completion is misaligned with safe execution, and violations accumulate with trajectory length; (ii) safety risks vary across domains, task types, and agent roles; (iii) most violations concentrate in resource access and inter-agent information transfer; and (iv) multi-agent collaboration expands the safety risk surface, while harness design sets the upper bound of safe deployment.
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