English(EN) Adjustable Text-Guided Backdoor Attacks with Natural-Word Triggers on Multimodal Pretrained Models

新型后门攻击利用自然语言触发器攻击多模态AI模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-14 04:00

研究人员开发了一种名为文本引导后门（TGB）的新型后门攻击，该攻击针对多模态预训练模型。与先前需要特定触发条件的前向攻击不同，TGB利用自然出现的词语作为触发器，使其更隐蔽且适用于现实场景。通过引入视觉对抗性扰动可以调整攻击的强度，从而在不改变被污染数据的情况下灵活控制其有效性。在组合图像检索和视觉问答等任务上的实验证明了TGB利用这些模型安全漏洞的能力。 AI

影响这项研究突显了多模态AI模型中存在的关键安全漏洞，可能影响其在现实应用中的安全部署。

排序理由该集群包含一篇详细介绍新型攻击方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍新型攻击方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, safety; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 50 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Yiyang Zhang, Chaojian Yu, Ziming Hong, Yuanjie Shao, Qinmu Peng, Tongliang Liu, Xinge You · 2026-08-14 04:00

具有自然词触发器的可调文本引导后门攻击在多模态预训练模型上

arXiv:2604.05809v2 Announce Type: replace-cross Abstract: This paper presents Text-Guided Backdoor (TGB), an adjustable backdoor attack against multimodal pretrained models that uses natural-word triggers, namely words that can naturally occur in ordinary textual inputs. Most exi…

本文如何被排名

报道来源 [1]

具有自然词触发器的可调文本引导后门攻击在多模态预训练模型上

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