English(EN) ARMs: Adaptive Red-Teaming Agent against Multimodal Models with Plug-and-Play Attacks

新的ARMs代理在Claude 4 Sonnet上实现了超过90%的攻击成功率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员开发了ARMs，一个自适应红队代理，旨在系统地评估视觉语言模型（VLMs）的安全漏洞。ARMs采用多样化的多步策略来诱导有害输出，实现了最先进的攻击成功率，在Claude 4 Sonnet上尤其超过了90%。该代理已被用于构建ARMs-Bench，一个包含超过30,000个红队测试实例的大型数据集，该数据集在用于安全微调时显著提高了VLM的鲁棒性。 AI

影响这项研究引入了一种识别和减轻多模态AI模型安全风险的新方法，有望带来更强大、更安全的AI系统。

排序理由该集群包含一篇研究论文，详细介绍了一种新的AI模型红队测试方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zhaorun Chen, Xun Liu, Mintong Kang, Jiawei Zhang, Minzhou Pan, Shuang Yang, Bo Li · 2026-11-06 05:00

ARMs：针对多模态模型的自适应红队代理，具有即插即用攻击

arXiv:2510.02677v2 Announce Type: replace Abstract: As vision-language models (VLMs) gain prominence, their multimodal interfaces also introduce new safety vulnerabilities, making the safety evaluation challenging and critical. Existing red-teaming efforts are either restricted t…

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报道来源 [1]

ARMs：针对多模态模型的自适应红队代理，具有即插即用攻击

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