New Jailbreak Attack Exploits Temporal Consistency in Text-to-Video Models
Researchers have introduced BSB, a structured jailbreak framework targeting text-to-video (T2V) models by exploiting temporal consistency—encoding harmful intent as transitions between otherwise harmless boundary states. Using Monte Carlo Tree Search in a textual proxy space, BSB achieves an average 18.6% relative gain in attack success rate over existing methods on models such as Veo 3.1, Sora 2, Seedance, and Kling v1. The study demonstrates that temporal consistency is a critical and previously underexplored vulnerability in T2V systems.
Why it matters: This work reveals a significant new attack surface in T2V models, raising important safety concerns as video generation technologies are increasingly adopted.
Full story at: arXiv Cryptography and Security ↗