这篇论文给AI Agent加了一道安全锁：用分布鲁棒优化算清楚策略违规的概率上限，比旧方法更准、更高效。
论文提出基于分布鲁棒优化（DRO）的框架，用于AI Agent在不确定性下的策略违反概率验证。该方法支持包含概率谓词或状态转换的Datalog策略，无需假设谓词间独立性。在终端和工具调用Agent标准基准上，计算出的概率上界比先前方法更紧，同时保证了安全与效用的权衡。实验表明，该方法在多个测试集上提升了安全策略的合规性。
Efficient and Sound Probabilistic Verification for AI Agents
Securing AI agents that operate in complex digital environments has become a critical need, and runtime monitoring approaches that formulate and enforce policies expressed in a formal language like Datalog offer a promising solution. However, existing approaches are restricted to deterministic policies. In many practical applications of AI agents, there is a need to enforce security policies in the face of ambiguity, leading to probabilistic predicates or state transitions (for example, a declassifier or Personally Identifiable Information (PII) detector that has some failure probability on each invocation). Furthermore, in many such applications, one cannot easily make the independence assumptions necessary to invoke prior work on probabilistic inference in Datalog. We address this by introducing a sound and efficient framework for such verification based on distributionally robust optimization, computing sound upper bounds on the probability of policy violation regardless of possible correlations between predicates. On standard benchmarks for terminal and tool calling agents, we demonstrate that our approach outperforms prior art and improves the security-utility trade-off while ensuring rigorous bounds on the probability of policy violation.