Paper
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
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 than 205$\times$ and inflates the inference latency by more than 15$\times$ relative to clean baselines. Simulations of real-time autonomous driving streams further reveal that this induced latency leads to critical safety violations. We urge the community to recognize and mitigate these high-hazard ELA in Video-LLMs.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.01454v1</id>\n <title>VidDoS: Universal Denial-of-Service Attack on Video-based Large Language Models</title>\n <updated>2026-03-02T05:11:47Z</updated>\n <link href='https://arxiv.org/abs/2603.01454v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.01454v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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 than 205$\\times$ and inflates the inference latency by more than 15$\\times$ relative to clean baselines. Simulations of real-time autonomous driving streams further reveal that this induced latency leads to critical safety violations. We urge the community to recognize and mitigate these high-hazard ELA in Video-LLMs.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <published>2026-03-02T05:11:47Z</published>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Duoxun Tang</name>\n </author>\n <author>\n <name>Dasen Dai</name>\n </author>\n <author>\n <name>Jiyao Wang</name>\n </author>\n <author>\n <name>Xiao Yang</name>\n </author>\n <author>\n <name>Jianyu Wang</name>\n </author>\n <author>\n <name>Siqi Cai</name>\n </author>\n </entry>"
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