The Temporal Moderation Gap: Text-to-Video Safety Filters Are Blind to Harm in Motion
Not on the map. Why?
Abstract (arXiv)
Text-to-video (T2V) services inherit their safety stack from image generation, pairing a keyword prompt filter with a per-frame checker that blocks a clip whenever one sampled frame looks unsafe. This stack has a blind spot unique to video. We prove that any moderator ignoring frame order accepts a harmful clip whenever it accepts that clip's benign shuffle, so harm carried by the ordering alone escapes. Empirically, the unmodified benchmark prompt already lands a clip in this moderation gap on 32.7% of Sequential-Action targets over four held-out seeds, and paraphrasing, scene splitting, and a feedback-driven prompt search show no significant improvement (paired McNemar p≥0.12), so prompt engineering is not needed to expose the vulnerability. Dense-scoring all 97 rendered frames shows that about a third of the delivered clips merely hide an unsafe frame, while the rest stay harmful as ordered videos even though every frame passes, an order-blind residual the unmodified prompt reaches on a quarter of Sequential-Action targets. We also document a measurement pitfall, since scoring a searched prompt on its own render seed inflates a 7.5% per-generation rate into an apparent 46.7%. A user study confirms that people read these clips as harmful and their shuffles as safe. The fix is to read frame order, and an order-aware detector separates these clips from their own shuffles at AUC 0.74 where per-frame checking sits at chance, which is the signal deployed moderation throws away.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 35 references that are in the map. First posted on arXiv 19 Aug 2026.
Builds on
25 of 35 references.
References · 25
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2025Qwen2.5-VL Technical Reportcited by 5,418
- 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,536
- 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformercited by 2,198
- 2025InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Modelscited by 1,769
- 2024HunyuanVideo: A Systematic Framework For Large Video Generative Modelscited by 1,597
- 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuningcited by 1,418
- 2022Expanding Language-Image Pretrained Models for General Video Recognitioncited by 320
- 2023MMA-Diffusion: MultiModal Attack on Diffusion Modelscited by 184
- 2023SneakyPrompt: Jailbreaking Text-to-image Generative Modelscited by 175
- 2022Revisiting the "Video" in Video-Language Understandingcited by 167
- 2023Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Promptscited by 161
13 more
- 2022Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?cited by 150
- 2024SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video Generationcited by 125
- 2024T2VSafetyBench: Evaluating the Safety of Text-to-Video Generative Modelscited by 66
- 2024GuardT2I: Defending Text-to-Image Models from Adversarial Promptscited by 61
- 2023VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Modelscited by 55
- 2024DiffZOO: A Purely Query-Based Black-Box Attack for Red-teaming Text-to-Image Generative Model via Zeroth Order Optimizationcited by 19
- 2025T2V-OptJail: Discrete Prompt Optimization for Text-to-Video Jailbreak Attackscited by 18
- 2025Jailbreaking on Text-to-Video Models via Scene Splitting Strategycited by 10
- 2025BadVideo: Stealthy Backdoor Attack against Text-to-Video Generationcited by 9
- 2025PLA: Prompt Learning Attack against Text-to-Image Generative Modelscited by 8
- 2025TEAR: Temporal-aware Automated Red-teaming for Text-to-Video Modelscited by 3
- 2026Two Frames Matter: A Temporal Attack for Text-to-Video Model Jailbreakingcited by 3
- 2026TrajShield: Trajectory-Level Safety Mediation for Defending Text-to-Video Models Against Jailbreak Attackscited by 1
Built on by
Not cited yet.