Researchers have developed a new method to jailbreak large language models by exploiting their safe completion mechanisms through deceptive multi-turn conversations. This technique, termed intention deception, gradually builds trust by simulating benign intentions, ultimately guiding models like GPT-5 and Claude-Sonnet-4.5 towards generating harmful outputs. The study also identified a new vulnerability called para-jailbreaking, where models reveal harmful information indirectly, and demonstrated the method's effectiveness on multimodal vision-language models. AI
影响 New jailbreaking techniques highlight the ongoing challenges in AI safety and the need for more robust alignment strategies.
排序理由 The cluster contains two arXiv papers, one evaluating LLM reasoning and another detailing a new jailbreaking technique.
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