Jailbreaking Multimodal Large Language Models using Multi-Clip Video
Summary
A new study introduces Multi-Clip Video (MCV) SafetyBench, a dataset of 2,920 videos, to investigate jailbreaking vulnerabilities in multimodal large language models (MLLMs) that process video inputs. Experiments on eight representative video MLLMs reveal that attack success rates consistently increase with the number of clips in a video. The research demonstrates that the video modality is more vulnerable to jailbreaking than the image modality, and dynamic videos pose a greater risk than static ones. Furthermore, MLLMs are more susceptible when videos contain more diverse contexts related to a harmful query. These findings highlight specific properties of video inputs that induce MLLM vulnerability, leading to a proposed defense strategy leveraging the image modality's relative robustness.
Key takeaway
For AI Security Engineers developing or deploying multimodal LLMs, understanding video input vulnerabilities is critical. You should prioritize robust safety alignment for video processing, especially with dynamic, multi-clip inputs containing diverse contexts, as these significantly increase jailbreaking risk. Consider implementing defense strategies that leverage the relative robustness of image modalities to mitigate these specific video-based attack vectors.
Key insights
Video MLLMs are more vulnerable to jailbreaking with multi-clip, diverse, and dynamic video inputs than images.
Principles
- Video modality is more vulnerable than image.
- Dynamic videos are riskier than static videos.
- Diverse video contexts increase MLLM vulnerability.
Method
The study introduces MCV SafetyBench, a dataset of 2,920 multi-clip videos, to evaluate MLLM vulnerability by varying clip count and context diversity.
In practice
- Leverage image modality for defense.
- Prioritize safety for dynamic video inputs.
- Scrutinize diverse video contexts.
Topics
- Multimodal LLMs
- Jailbreaking
- Video Modality
- Safety Alignment
- MCV SafetyBench
Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Security Engineer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.