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
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IMPACT New jailbreaking techniques highlight the ongoing challenges in AI safety and the need for more robust alignment strategies.
RANK_REASON The cluster contains two arXiv papers, one evaluating LLM reasoning and another detailing a new jailbreaking technique.