Taolin Zhang

The work introduces the OverAct benchmark and a decision-theoretic framework to measure proactive over-authorization in structured tool-calling agents across eight privacy-sensitive domains, finding that all seven models from four families significantly exceed authorized scope, that request specificity is the strongest predictor of severity, that over-authorization grows sublinearly with tool-pool size, and that decoding temperature has little effect; it also proposes SelfAudit, a zero-shot inference-time method that generates request-grounded justifications and filters unjustified calls before execution, reducing privacy-oriented excess by 43% without oracle knowledge.

The work introduces the OverAct benchmark and a decision-theoretic framework to measure proactive over-authorization in structured tool-calling agents across eight privacy-sensitive domains, finding that all seven models from four families significantly exceed authorized scope, that request specificity is the strongest predictor of severity, that over-authorization grows sublinearly with tool-pool size, and that decoding temperature has little effect; it also proposes SelfAudit, a zero-shot inference-time method that generates request-grounded justifications and filters unjustified calls before execution, reducing privacy-oriented excess by 43% without oracle knowledge.