[2603.01454] VidDoS: Universal Denial-of-Service Attack on Video-based Large Language Models

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.01454 (cs) [Submitted on 2 Mar 2026] Title:VidDoS: Universal Denial-of-Service Attack on Video-based Large Language Models Authors:Duoxun Tang, Dasen Dai, Jiyao Wang, Xiao Yang, Jianyu Wang, Siqi Cai View a PDF of the paper titled VidDoS: Universal Denial-of-Service Attack on Video-based Large Language Models, by Duoxun Tang and 4 other authors View PDF HTML (experimental) Abstract:Video-LLMs are increasingly deployed in safety-critical applications but are vulnerable to Energy-Latency Attacks (ELAs) that exhaust computational resources. Current image-centric methods fail because temporal aggregation mechanisms dilute individual frame perturbations. Additionally, real-time demands make instance-wise optimization impractical for continuous video streams. We introduce VidDoS, which is the first universal ELA framework tailored for Video-LLMs. Our method leverages universal optimization to create instance-agnostic triggers that require no inference-time gradient calculation. We achieve this through $\textit{masked teacher forcing}$ to steer models toward expensive target sequences, combined with a $\textit{refusal penalty}$ and $\textit{early-termination suppression}$ to override conciseness priors. Testing across three mainstream Video-LLMs and three video datasets, which include video question answering and autonomous driving scenarios, shows extreme degradation. VidDoS induces a token expansion of more t...