AI RESEARCH
An Empirical Study of Multi-Generation Sampling for Jailbreak Detection in Large Language Models
arXiv CS.LG
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ArXi:2604.18775v1 Announce Type: cross Detecting jailbreak behaviour in large language models remains challenging, particularly when strongly aligned models produce harmful outputs only rarely. In this work, we present an empirical study of output based jailbreak detection under realistic conditions using the JailbreakBench Behaviors dataset and multiple generator models with varying alignment strengths. We evaluate both a lexical TF-IDF detector and a generation inconsistency based detector across different sampling budgets.