Characterizing Statistical Separability in TP-CRIV for Probabilistic AI Models

Summary

This paper characterizes statistical separability in third-party challenge-response identity verification (TP-CRIV) for probabilistic AI models, where repeated queries can yield different outputs. It relates challenge-wise behavior of matching and non-matching provers to verification-level separability, showing how the numbers of independent challenges and repeated responses affect detection performance and how the verification budget for a target AUC can be estimated. The approach is instantiated for LLMs with open-ended challenges, with experiments reporting close agreement between theoretical and empirical AUCs.