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New backdoor attack exploits multimodal AI models with natural language triggers

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new type of backdoor attack called Text-Guided Backdoor (TGB) that targets multimodal pretrained models. Unlike previous attacks that require specific trigger conditions, TGB utilizes naturally occurring words as triggers, making it more stealthy and practical for real-world scenarios. The attack's strength can be adjusted by introducing visual adversarial perturbations, allowing for flexible control over its effectiveness without altering the poisoned data. Experiments on tasks like Composed Image Retrieval and Visual Question Answering demonstrate TGB's ability to exploit security vulnerabilities in these models. AI

IMPACT This research highlights critical security vulnerabilities in multimodal AI models, potentially impacting their safe deployment in real-world applications.

RANK_REASON The cluster contains a research paper detailing a new attack method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New backdoor attack exploits multimodal AI models with natural language triggers

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The cluster contains a research paper detailing a new attack method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yiyang Zhang, Chaojian Yu, Ziming Hong, Yuanjie Shao, Qinmu Peng, Tongliang Liu, Xinge You ·

    Adjustable Text-Guided Backdoor Attacks with Natural-Word Triggers on Multimodal Pretrained Models

    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…

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