标题： CAVALRY-V：视频多模态大语言模型对抗攻击的大型生成框架 显示英文标题

标题： CAVALRY-V: A Large-Scale Generator Framework for Adversarial Attacks on Video MLLMs

摘要： 视频多模态大语言模型（V-MLLMs）在时间推理和跨模态理解方面表现出色，但由于独特的挑战：复杂的跨模态推理机制、时间依赖性和计算约束，它们对对抗攻击的脆弱性仍缺乏深入研究。我们提出了CAVALRY-V（跨模态语言-视觉对抗生成用于视频），一种新的框架，直接针对视频多模态大语言模型中视觉感知与语言生成之间的关键接口。我们的方法引入了两个关键创新：(1) 一个双目标语义-视觉损失函数，同时干扰模型的文本生成logits和视觉表示，以破坏跨模态整合，(2) 一个计算高效的两阶段生成器框架，结合大规模预训练以实现跨模型可迁移性，并通过专门微调以实现时空一致性。在全面的视频理解基准上的实证评估表明，CAVALRY-V显著优于现有的攻击方法，在商业系统（GPT-4.1，Gemini 2.0）和开源模型（QwenVL-2.5，InternVL-2.5，Llava-Video，Aria，MiniCPM-o-2.6）上，其平均提升达到22.8%。我们的框架通过隐式时间一致性建模而非显式正则化实现灵活性，即使在图像理解任务上也能显著提升性能（平均提升34.4%）。这种能力展示了CAVALRY-V作为跨多模态系统对抗研究基础方法的潜力。 显示英文摘要

摘要： Video Multimodal Large Language Models (V-MLLMs) have shown impressive capabilities in temporal reasoning and cross-modal understanding, yet their vulnerability to adversarial attacks remains underexplored due to unique challenges: complex cross-modal reasoning mechanisms, temporal dependencies, and computational constraints. We present CAVALRY-V (Cross-modal Language-Vision Adversarial Yielding for Videos), a novel framework that directly targets the critical interface between visual perception and language generation in V-MLLMs. Our approach introduces two key innovations: (1) a dual-objective semantic-visual loss function that simultaneously disrupts the model's text generation logits and visual representations to undermine cross-modal integration, and (2) a computationally efficient two-stage generator framework that combines large-scale pre-training for cross-model transferability with specialized fine-tuning for spatiotemporal coherence. Empirical evaluation on comprehensive video understanding benchmarks demonstrates that CAVALRY-V significantly outperforms existing attack methods, achieving 22.8% average improvement over the best baseline attacks on both commercial systems (GPT-4.1, Gemini 2.0) and open-source models (QwenVL-2.5, InternVL-2.5, Llava-Video, Aria, MiniCPM-o-2.6). Our framework achieves flexibility through implicit temporal coherence modeling rather than explicit regularization, enabling significant performance improvements even on image understanding (34.4% average gain). This capability demonstrates CAVALRY-V's potential as a foundational approach for adversarial research across multimodal systems.