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Oct 12 · Monday

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  1. AttacksarXiv 2607.02961

    研究者提出信息过载方法越狱大型视觉语言模型

    提出 INFER+ 信息过载方法,越狱大型视觉语言模型

    Published
    Evaluated models
    Qwen3-VL-8B, Qwen2-VL-7B, InternVL3.5-8B and others8
    Abstract用图文过载越狱 LVLM。

    这篇工作用信息过载越狱 LVLM,而不是靠 OOD 变换隐藏意图。

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  2. AttacksarXiv 2607.21619

    ASO:用GRPO优化视觉风格放大MLLM越狱攻击

    提出 ASO,用 GRPO 优化视觉风格以放大越狱攻击

    Published
    Attacker
    Gray-box
    Evaluated models
    GPT-4.1-mini, Gemini-2.5-Flash, Qwen3-VL and others6

    Abstract论文揭示了 MLLM 的风格敏感性脆弱面,提出 ASO 框架利用强化学习优化风格触发器,有效放大了现有视觉越狱效果。

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  3. AttacksarXiv 2604.03117

    Unveiling Physical-World Semantic Vulnerabilities: Universal Adversarial Patches for Infrared Vision-Language Models

    Proposes the universal curved mesh patch UCGP, causing cross-task semantic bias in infrared vision-language models.

    Published
    Attacker
    Gray-box
    Evaluated models
    OpenAI CLIP ViT-L/14, OpenCLIP ViT-B/16, Meta-CLIP ViT-L/14 and others10
    AbstractUCGP uses one shared curved-grid patch to simultaneously lower the classification and open-ended outputs of IR-VLM.

    UCGP is a universal physical adversarial patch for infrared vision-language models. The question is: can a patch that can be attached and reused across samples cause stable semantic deviations in the infrared visual evidence shared by classification, description, and VQA. The patch is constrained to a low-frequency curved mesh.

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  4. AttacksarXiv 2601.21531

    Adversarial robustness of large vision-language models under visual token compression

    提出 CAGE 攻击,在视觉 token 压缩下用图像扰动使预测偏离

    Published
    Attacker
    Gray-box
    Evaluated models
    LLaVA, Qwen2.5-VL, InternVL3-8B and others5
    AbstractCAGE 对齐压缩瓶颈后,压缩 LVLM 的稳健准确率低于全 token 编码器攻击。

    CAGE 研究的是:LVLM 在推理时剪枝或合并视觉 token 之后,对抗稳健性该怎么估。

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