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New attack method improves adversarial transferability against closed-source MLLMs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called IAU-FOA to improve the effectiveness of adversarial attacks against closed-source multimodal large language models (MLLMs). This technique enhances the transferability of adversarial examples by aligning features at both global and local levels, addressing limitations of previous methods that primarily used global image-level features. IAU-FOA incorporates confidence-adaptive unbalanced transport for fine-grained feature alignment and visual-invariance augmentation to ensure adversarial perturbations generalize across different visual encoders, demonstrating superior performance over existing methods. AI

IMPACT This research could lead to more robust defenses against adversarial attacks on multimodal AI systems.

RANK_REASON The cluster contains a research paper detailing a new method for adversarial attacks on MLLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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

New attack method improves adversarial transferability against closed-source MLLMs

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The cluster contains a research paper detailing a new method for adversarial attacks on MLLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiaojun Jia, Simeng Qin, Yiming Li, Jie Liao, Sensen Gao, Ke Ma, Yang Liu, Xiaochun Cao ·

    Visual-Invariance-Augmented Feature Optimal Alignment for Transferable Adversarial Attacks against Closed-Source MLLMs

    arXiv:2610.06977v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) remain vulnerable to transferable adversarial examples, especially in black-box settings where only open-source surrogate models are accessible. Existing targeted transfer attacks mainly al…

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