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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 nu","title":"The Great Pretender: A Stochasticity Problem in LLM Jailbreak","url":"https://arxiv.org/abs/2605.14418","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.14418v1 Announce Type: cross \nAbstract: \"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. 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