Two Frames Matter: A Temporal Attack for Text-to-Video Model Jailbreaking
Recent text-to-video (T2V) models can synthesize complex videos from lightweight natural language prompts, raising urgent concerns about safety alignment in the event of misuse in the real world. Prior jailbreak attacks typically rewrite unsafe prompts into paraphrases that evade content filters while preserving meaning. Yet, these approaches often still retain explicit sensitive cues in the input text and therefore overlook a more profound, video-specific weakness. In this paper, we identify a temporal trajectory infilling vulnerability of T2V systems under fragmented prompts: when the prompt specifies only sparse boundary conditions (e.g., start and end frames) and leaves the intermedia...
| Authors | Moyang Chen, Zonghao Ying, Wenzhuo Xu et al. |
|---|---|
| Primary category | cs.CR |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
Connected in the knowledge graph
- rux22/bordair-multimodalDataset · cited by
- jinidnain/bordair-multimodalDataset · cited by
- Bordair/bordair-multimodalDataset · cited by
- sulpikar2/bordair-multimodalDataset · cited by
Models and datasets referencing this paper
- jinidnain/bordair-multimodalDataset
- sulpikar2/bordair-multimodalDataset
- rux22/bordair-multimodalDataset
- Bordair/bordair-multimodalDataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.