arXiv AI
SceneJail: Exploiting Video Scenario Context to Jailbreak Multimodal LLMs
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arXiv AI
Aug 21
TempJail: Temporal Jailbreak Attack against Large Vision-Language Models via Subtitle Scheduling
arXiv:2608. 19737v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) have achieved remarkable progress in video understanding and reasoning.
Hugging Face Trending Papers
Aug 20
TempJail: Temporal Jailbreak Attack against Large Vision-Language Models via Subtitle Scheduling
Large vision-language models (LVLMs) have achieved remarkable progress in video understanding and reasoning. Despite extensive studies on text- and image-based jailbreaks, video jailbreaks against LVL...
arXiv AI
Jul 21
Between Safe Boundaries: Exploiting Temporal Consistency for Jailbreaking Text-To-Video Generation Models
arXiv:2607. 17279v1 Announce Type: cross Abstract: Recently, text-to-video (T2V) models have been widely deployed, sparking growing concerns over their robustness against jailbreak attacks.
arXiv AI
Jun 2
Jailbreaking Multimodal Large Language Models using Multi-Clip Video
arXiv:2606. 02111v1 Announce Type: cross Abstract: As multimodal large language models (MLLMs) have advanced to process video inputs, concerns have emerged about their potential for malicious misuse.
arXiv Computer Vision
Aug 28
TempJail: Temporal Jailbreak Attacks against Image-to-Video Generation Models
The paper introduces TempJail, a temporal jailbreak framework targeting image‑to‑video generation models. It exploits a newly identified vulnerability where unsafe semantics arise from the composition of frames over time, rather than from single‑frame violations. By decomposing malicious captions into visual conditions and temporal instructions, and by employing controlled latent perturbations and template rewriting, TempJail achieves a 23.3 % higher attack success rate than prior methods on several commercial models.
arXiv Computer Vision
Sep 23
Probabilistic Modeling of Jailbreak on Multimodal LLMs: From Quantification to Application
arXiv:2503.06989v5 Announce Type: replace-cross Abstract: Recently, Multimodal Large Language Models (MLLMs) have demonstrated their superior ability in understanding multimodal content. However, the...