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arXiv · 2601.21220

LAMP: Learning Universal Adversarial Perturbations for Multi-Image Tasks via Pre-trained Models

Alvi Md Ishmam·Najibul Haque Sarker·Zaber Ibn Abdul Hakim·Chris Thomas
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Abstract

Multimodal Large Language Models (MLLMs) have achieved remarkable performance across vision-language tasks. Recent advancements allow these models to process multiple images as inputs. However, the vulnerabilities of multi-image MLLMs remain unexplored. Existing adversarial attacks focus on single-image settings and often assume a white-box threat model, which is impractical in many real-world scenarios. This paper introduces LAMP, a black-box method for learning Universal Adversarial Perturbations (UAPs) targeting multi-image MLLMs. LAMP applies an attention-based constraint that prevents the model from effectively aggregating information across images. LAMP also introduces a novel cross-image contagious constraint that forces perturbed tokens to influence clean tokens, spreading adversarial effects without requiring all inputs to be modified. Additionally, an index-attention suppression loss enables a robust position-invariant attack. Experimental results show that LAMP outperforms SOTA baselines and achieves the highest attack success rates across multiple vision-language tasks and models.

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Alvi Md Ishmam, Najibul Haque Sarker, Zaber Ibn Abdul Hakim, Chris Thomas. 2026-01-29. LAMP: Learning Universal Adversarial Perturbations for Multi-Image Tasks via Pre-trained Models. https://arxiv.org/abs/2601.21220

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