JailMeter: An Evidence-Based Evaluation Framework for Jailbreak Attacks on Large Language Models
The assessment of jailbreak attacks against large language models currently suffers from inconsistent evaluation criteria and methods, leading to unreliable estimates of attack success rates. We propose JailMeter, an evidence-based evaluation framework designed to more faithfully measure jailbreak effectiveness. Inspired by the Information Bottleneck theory, JailMeter applies dual-feedback optimization to filter jailbreak noise from model responses while preserving content relevant to the original malicious question. This process produces concise evidence for a rigorous assessment under which an attack is validated only when the response captures the malicious intent and delivers a comple...
| Authors | Qingjia Huang, Jingyu Zhang, Jianguo Wu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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