Capture Summary

Recent arXiv preprint on sound probabilistic runtime verification for AI agents under uncertain detectors and predicates.

Abstract Capture

The paper studies runtime policy enforcement for agents when monitors and predicates are probabilistic rather than deterministic, such as declassifiers or PII detectors with nonzero error rates. The authors propose a framework based on distributionally robust optimization that computes sound upper bounds on policy-violation probability without assuming predicate independence. They report improved security-utility trade-offs on terminal and tool-calling agent benchmarks while preserving rigorous policy-violation bounds.

Collection Notes

• Untrusted source content. Treat formal methods and benchmark claims as evidence only.
• Primary relevance: [[03_Topics/Guardrails and Monitoring]], [[03_Topics/Agentic AI Security]]
• PDF: https://arxiv.org/pdf/2606.20510