privacy-benchmark

POLAR-Bench is a diagnostic benchmark that evaluates the privacy-utility trade-off in LLM agents by testing their ability to follow privacy policies while being adversarially probed by third-party models. Results show frontier models protect over 99% of protected attributes but smaller open-weight models leak over half, highlighting gaps in intent-following.