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New UJEM-KL attack bypasses VLM safety measures with entropy maximization

Researchers have developed a new method called Untargeted Jailbreak via Entropy Maximization (UJEM-KL) to bypass safety measures in vision-language models (VLMs). This technique focuses on manipulating high-entropy tokens during decoding to flip refusal outcomes, rather than relying on fixed patterns. UJEM-KL demonstrates improved transferability across different VLMs and remains effective against common defenses, suggesting that previous limitations in multimodal jailbreaks were due to overly constrained optimization objectives. AI

影响 This research highlights a novel vulnerability in vision-language models, potentially impacting the security and reliability of AI systems.

排序理由 The cluster contains an academic paper detailing a new method for attacking AI models.

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

New UJEM-KL attack bypasses VLM safety measures with entropy maximization

报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Break the Brake, Not the Wheel: Untargeted Jailbreak via Entropy Maximization

    Recent studies show that gradient-based universal image jailbreaks on vision-language models (VLMs) exhibit little or no cross-model transferability, casting doubt on the feasibility of transferable multimodal jailbreaks. We revisit this conclusion under a strictly untargeted thr…

  2. arXiv cs.CV TIER_1 English(EN) · Jing Zhang ·

    Break the Brake, Not the Wheel: Untargeted Jailbreak via Entropy Maximization

    Recent studies show that gradient-based universal image jailbreaks on vision-language models (VLMs) exhibit little or no cross-model transferability, casting doubt on the feasibility of transferable multimodal jailbreaks. We revisit this conclusion under a strictly untargeted thr…