# Between Safe Boundaries: Exploiting Temporal Consistency for Jailbreaking Text-To-Video Generation Models

arXiv: https://arxiv.org/abs/2607.17279 · HTML: https://mlsanity.com/paper/2607.17279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Vision-language and text-to-image safety, one of 12 directions in LLM safety and security.
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 19 Jul 2026.

## Placed in

- LLM safety and security / [Vision-language and text-to-image safety](https://mlsanity.com/safety/vision-language-and-text-to-image-safety/)

## Builds on (most cited first, 11 of 11)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://mlsanity.com/paper/2303.13439/) (2023)
- [SneakyPrompt: Jailbreaking Text-to-image Generative Models](https://mlsanity.com/paper/2305.12082/) (2023)
- [SurrogatePrompt: Bypassing the Safety Filter of Text-to-Image Models via Substitution](https://mlsanity.com/paper/2309.14122/) (2023)
- [T2V-OptJail: Discrete Prompt Optimization for Text-to-Video Jailbreak Attacks](https://mlsanity.com/paper/2505.06679/) (2025)
- [Jailbreaking on Text-to-Video Models via Scene Splitting Strategy](https://mlsanity.com/paper/2509.22292/) (2025)
- [DiffGuard: Text-Based Safety Checker for Diffusion Models](https://mlsanity.com/paper/2412.00064/) (2024)
- [RunawayEvil: Jailbreaking the Image-to-Video Generative Models](https://mlsanity.com/paper/2512.06674/) (2025)
- [SPARK: Jailbreaking T2V Models by Synergistically Prompting Auditory and Recontextualized Knowledge](https://mlsanity.com/paper/2511.13127/) (2025)

## Built on by (most cited first, 1 of 1)

- [TempQ-Jail: Query-Constrained Candidate Ranking for Text-to-Video Jailbreak Attacks](https://mlsanity.com/paper/2609.31032/) (2026)

## Abstract (arXiv)

Recently, text-to-video (T2V) models have been widely deployed, sparking growing concerns over their robustness against jailbreak attacks. Existing jailbreak methods, mostly adapted from text-to-image attacks, suffer notable drawbacks when applied to T2V systems. They fail to fully leverage temporal consistency, an inherent characteristic of video generation. Besides, these methods demand heavy video query optimization, which is infeasible in practical black-box scenarios. Their adversarial prompt search is also driven by heuristic local signals, lacking principled structured exploration strategies. To tackle these limitations, we propose BSB, a structured, query-efficient jailbreak framework for T2V models. BSB harnesses temporal consistency by encoding harmful intent as the transition between two individually harmless boundary states. Under this paradigm, the attack targets boundary-state pairs whose interpolation tends to produce unsafe intermediate frames during video generation. Directly evaluating all candidate pairs within the video space incurs prohibitive computation cost. Instead, BSB conducts Monte Carlo Tree Search (MCTS) in a cheaper textual proxy space and regularly calibrates search outcomes with sparse video-level evaluations. We conduct comprehensive experiments on mainstream commercial T2V models including Veo 3.1, Sora 2, Seedance and Kling v1. Results show BSB surpasses all existing jailbreak baselines, delivering an average 18.6% relative gain in attack success rate over the strongest competitor across evaluated models. Our findings identify temporal consistency as an understudied yet vital attack surface for T2V models and verify that structured search facilitates effective vulnerability discovery under constrained query budgets.