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English(EN) The Alignment Curse: Modality Alignment Supercharges Audio Attacks via Text Transfer

新研究发现模态对齐会迁移人工智能音频攻击

作者 PulseAugur 编辑部 · [1 个来源] ·

一篇新研究论文引入了“对齐诅咒”原则，该原则证明了全模态模型中改进的文本-音频模态对齐如何会无意中将安全漏洞从文本迁移到音频。研究人员发现，文本迁移的音频攻击与仅音频攻击一样有效，甚至常常更优越，这表明当前的音频安全评估可能低估了风险。该研究分析了Qwen2.5-Omni和Qwen3-Omni等模型，发现更紧密的模态对齐与更有效的跨模态攻击迁移之间存在持续的相关性。 AI

影响 突显了人工智能能力与安全之间的根本性矛盾，表明当前的音频安全措施可能不足。

排序理由 介绍新原则和经验性发现的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新研究发现模态对齐会迁移人工智能音频攻击

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介绍新原则和经验性发现的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yupeng Chen, Junchi Yu, Aoxi Liu, Baoyuan Wu, Philip Torr, Adel Bibi ·

    对齐诅咒：模态对齐通过文本迁移超级增强音频攻击

    arXiv:2602.02557v2 Announce Type: replace-cross Abstract: Recent advances in end-to-end trained omni-models have substantially improved audio capabilities by strengthening text-audio modality alignment. However, whether such alignment inadvertently facilitates the transfer of saf…

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