做LLM安全评估的团队会发现现有ASR指标不可靠——论文用数据证明80%的ASR在连续测试中可能只剩50%，CAS-eval和CAS-gen直接解决了这个评估和生成的不一致问题，做红队测试的建议点开。
该论文揭示了LLM越狱攻击评估中的关键问题：攻击成功率（ASR）作为主要指标并不稳定，导致已发表的ASR数值被系统性夸大且不可比较。研究发现，即使一个越狱提示在单次测试中达到80%的ASR，在连续5次尝试中成功率可能降至50%。作者分析了攻击生成和评估过程中的随机性影响，提出了新指标CAS-eval和攻击生成框架CAS-gen。CAS-eval能更稳定地评估攻击效果，而CAS-gen帮助恢复因随机性导致的30个百分点的ASR损失。这项工作对越狱攻击的可靠评估和防御研究具有重要参考价值。
The Great Pretender: A Stochasticity Problem in LLM Jailbreak
"Oh-Oh, yes, I'm the great pretender. Pretending that I'm doing well. My need is such, I pretend too much..." summarizes the state in the area of jailbreak creation and evaluation. You find this method to generate adversarial attacks proposed by a reputable institution (e.g., BoN from Anthropic or Crescendo from Microsoft Research). However, this method does not deliver on the promise claimed in the paper despite having top ASR scores against industry-grade LLMs. You successfully generate the jailbreak prompts against your target (open) model. However, the generated jailbreak prompt works against the target model with a 50% consecutive success rate (5 out of 10 attempts) despite having an 80% ASR (on paper) on the latest closed-source model (with a guardrail system)! This observation leads us to think. First, Attack Success Rate (ASR), the primary metric for LLM jailbreak benchmarking, is not a stable quantity. Second, published ASR numbers are therefore systematically inflated and incomparable across papers. Therefore, we wonder "Why a successful jailbreak prompt does not perform consistently well against a target model on which the prompts have been optimized?". To answer this question, we study the impact of stochasticity not only during attack evaluation but also during attack generation. Our evaluation includes several jailbreak attacks, models (different sizes and providers), and judges. In addition, we propose a new metric and two new frameworks (CAS-eval and CAS-gen). Our evaluation framework, CAS-eval, shows that an attack can have an ASR drop of up to 30 percentage points when a jailbreak prompt needs to succeed on more than one attempt. Thankfully, our attack generation framework (CAS-gen) improves previous jailbreak methods and helps them recover this loss of 30 percentage points!