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English(EN) !Imperio, smolVLA: The Implications of Data Poisoning on Open Source Robotics

数据投毒攻击被发现对开源机器人模型具有实用性和隐蔽性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员已经证明，对开源机器人模型进行数据投毒攻击是实用且隐蔽的。通过在训练数据中注入少量受污染的样本，可以在像smolVLA这样的视觉语言动作模型中嵌入后门。即使在使用干净的提示时，此后门也可以根据指令禁用机器人或显著降低其在特定任务上的性能。这些发现突显了在开源机器人技术中需要更加关注数据集的来源。 AI

影响 凸显了开源AI模型中关键的安全漏洞，有必要在机器人技术中改进数据集来源和安全实践。

排序理由 该集群包含一篇详细介绍AI模型新型安全漏洞的研究论文。

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数据投毒攻击被发现对开源机器人模型具有实用性和隐蔽性

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Stefan B\"uhler, Mark Schutera ·

    !Imperio, smolVLA：数据投毒对开源机器人技术的影响

    arXiv:2607.04146v1 Announce Type: cross Abstract: This work establishes that trigger-word data poisoning of vision language action models is practical, while at the same time the open-source robotics ecosystem holds trust assumptions about community contributions. A few poisoned …

  2. arXiv cs.CL TIER_1 English(EN) · Mark Schutera ·

    !Imperio, smolVLA：数据投毒对开源机器人技术的影响

    This work establishes that trigger-word data poisoning of vision language action models is practical, while at the same time the open-source robotics ecosystem holds trust assumptions about community contributions. A few poisoned samples can silently embed a backdoor that disable…

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