mlsanityData to 7 Oct 2026

The Temporal Moderation Gap: Text-to-Video Safety Filters Are Blind to Harm in Motion

arXiv 2609.26233 ↗First posted 19 Aug 2026cs.CVcited by 0

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

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2025Qwen2.5-VL Technical Reportcited by 5,418
  3. 2025Wan: Open and Advanced Large-Scale Video Generative Modelscited by 2,536
  4. 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformercited by 2,198
  5. 2025InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Modelscited by 1,769
  6. 2024HunyuanVideo: A Systematic Framework For Large Video Generative Modelscited by 1,597
  7. 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuningcited by 1,418
  8. 2022Expanding Language-Image Pretrained Models for General Video Recognitioncited by 320
  9. 2023MMA-Diffusion: MultiModal Attack on Diffusion Modelscited by 184
  10. 2023SneakyPrompt: Jailbreaking Text-to-image Generative Modelscited by 175
  11. 2022Revisiting the "Video" in Video-Language Understandingcited by 167
  12. 2023Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Promptscited by 161
13 more
  1. 2022Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?cited by 150
  2. 2024SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video Generationcited by 125
  3. 2024T2VSafetyBench: Evaluating the Safety of Text-to-Video Generative Modelscited by 66
  4. 2024GuardT2I: Defending Text-to-Image Models from Adversarial Promptscited by 61
  5. 2023VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Modelscited by 55
  6. 2024DiffZOO: A Purely Query-Based Black-Box Attack for Red-teaming Text-to-Image Generative Model via Zeroth Order Optimizationcited by 19
  7. 2025T2V-OptJail: Discrete Prompt Optimization for Text-to-Video Jailbreak Attackscited by 18
  8. 2025Jailbreaking on Text-to-Video Models via Scene Splitting Strategycited by 10
  9. 2025BadVideo: Stealthy Backdoor Attack against Text-to-Video Generationcited by 9
  10. 2025PLA: Prompt Learning Attack against Text-to-Image Generative Modelscited by 8
  11. 2025TEAR: Temporal-aware Automated Red-teaming for Text-to-Video Modelscited by 3
  12. 2026Two Frames Matter: A Temporal Attack for Text-to-Video Model Jailbreakingcited by 3
  13. 2026TrajShield: Trajectory-Level Safety Mediation for Defending Text-to-Video Models Against Jailbreak Attackscited by 1

Built on by

Not cited yet.