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新的VLA-Hijack攻击利用了AI模型的视觉自我定位能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了VLA-Hijack，一个新颖的对抗性框架，旨在利用视觉-语言-动作（VLA）模型的漏洞。该方法通过创建“幻影具身”，利用机器人手臂的视觉自我定位能力来破坏其运动规划能力。VLA-Hijack在白盒场景中表现出更高的效率，并在黑盒设置中跨不同模型架构和领域展现出更优越的可迁移性。 AI

影响 这项研究揭示了VLA模型的一个关键漏洞，可能影响其在现实世界机器人应用中的安全部署。

排序理由 该集群包含一篇详细介绍针对AI模型的新型对抗性攻击方法的学术论文。

在 arXiv cs.CV 阅读 →

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新的VLA-Hijack攻击利用了AI模型的视觉自我定位能力

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jiyuan Fu, Kaixun Jiang, Jingkai Jia, Zhaoyu Chen, Xueyao Chen, Lingyi Hong, Shuyong Gao, Chenzhi Tan, Dingkang Yang, Wenqiang Zhang ·

    VLA-Hijack: A Transferable Patch Attack against Vision-Language-Action Models via Visual Proprioception Hijacking

    arXiv:2605.28083v1 Announce Type: new Abstract: While Vision-Language-Action (VLA) models have emerged as powerful generalist policies, their severe vulnerability to adversarial patches significantly hinders their deployment in safety-critical domains. Moreover, existing patch at…

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

    VLA-Hijack: A Transferable Patch Attack against Vision-Language-Action Models via Visual Proprioception Hijacking

    While Vision-Language-Action (VLA) models have emerged as powerful generalist policies, their severe vulnerability to adversarial patches significantly hinders their deployment in safety-critical domains. Moreover, existing patch attacks primarily focus on white-box settings, hea…

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