  Despite the remarkable performance of video-based large language models
(LLMs), their adversarial threat remains unexplored. To fill this gap, we
propose the first adversarial attack tailored for video-based LLMs by crafting
flow-based multi-modal adversarial perturbations on a small fraction of frames
within a video, dubbed FMM-Attack. Extensive experiments show that our attack
can effectively induce video-based LLMs to generate incorrect answers when
videos are added with imperceptible adversarial perturbations. Intriguingly,
our FMM-Attack can also induce garbling in the model output, prompting
video-based LLMs to hallucinate. Overall, our observations inspire a further
understanding of multi-modal robustness and safety-related feature alignment
across different modalities, which is of great importance for various large
multi-modal models. Our code is available at
https://github.com/THU-Kingmin/FMM-Attack.
