TrajShield: Trajectory-Level Safety Mediation for Defending Text-to-Video Models Against Jailbreak Attacks

• URL: http://arxiv.org/abs/2605.01761v1
• Date: Sun, 03 May 2026 07:49:51 GMT
• Title: TrajShield: Trajectory-Level Safety Mediation for Defending Text-to-Video Models Against Jailbreak Attacks
• Abstract summary: We propose a training-free, inference-time defense framework that reformulates T2V safety as a causal intervention in a temporally structured semantic space.<n>TrajShield handles explicit unsafe prompts, jailbreak attacks, and temporally emergent risks in a unified manner.
• Score: 3.3681515336200403
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text-to-Video (T2V) models have demonstrated remarkable capability in generating temporally coherent videos from natural language prompts, yet they also risk producing unsafe content such as violence or explicit material. Existing prompt-level defenses are largely inherited from text-to-image safety and operate on the lexical surface of the input, making them vulnerable to jailbreak attacks that disguise harmful intent through rephrasing or adversarial prompting. Moreover, T2V generation introduces a distinctive challenge overlooked by prior work: temporally emergent risk, where a seemingly benign prompt leads to unsafe content through the generator's temporal extrapolation toward narrative coherence. We propose \method{}, a training-free, inference-time defense framework that reformulates T2V safety as a causal intervention in a temporally structured semantic space. TrajShield handles explicit unsafe prompts, jailbreak attacks, and temporally emergent risks in a unified manner by simulating the implied trajectory of a prompt, localizing the causal origin of potential risk, and applying a minimally invasive rewrite that neutralizes the risk while preserving safety-irrelevant semantics. Experiments on T2VSafetyBench across 14 safety categories and multiple T2V backends demonstrate that TrajShield achieves state-of-the-art defenseive performance while maintaining high semantic fidelity, substantially outperforming existing defenses, with an average ASR reduction of 52.44\%.

Related papers

• SafeCA: Safe Cross-Attention Localization and Regulation for Text-to-Video Jailbreak Defense [77.70598852234149]
  Text-to-Video (T2V) generative models are vulnerable to jailbreak attacks in real-world deployment.<n>We propose SafeCA, a feature-level defense mechanism for safe cross-attention localization and regularization.<n> Experimental results show that SafeCA reduces the jailbreak success rate by about 20% on mainstream T2V models.
  arXiv  Detail & Related papers  (2026-08-11T14:01:24Z)
• HazardArena: Evaluating Semantic Safety in Vision-Language-Action Models [87.35765363039638]
  We introduce HazardArena, a benchmark designed to evaluate semantic safety in Vision-Language-Action models.<n>We find that VLA models trained exclusively on safe scenarios often fail to behave safely when evaluated in their corresponding unsafe counterparts.<n>We propose a training-free Safety Option Layer that constrains action execution using semantic attributes or a vision-language judge.
  arXiv  Detail & Related papers  (2026-04-14T08:32:02Z)
• DTVI: Dual-Stage Textual and Visual Intervention for Safe Text-to-Image Generation [10.071877020102557]
  Text-to-Image (T2I) diffusion models have demonstrated strong generation ability, but their potential to generate unsafe content raises significant safety concerns.<n>We propose DTVI, a dual-stage inference-time defense framework for safe T2I generation.
  arXiv  Detail & Related papers  (2026-03-23T14:41:11Z)
• Two Frames Matter: A Temporal Attack for Text-to-Video Model Jailbreaking [3.1471354211996956]
  Recent text-to-video (T2V) models can synthesize complex videos from lightweight natural language prompts.<n>Prior jailbreak attacks typically rewrite unsafe prompts into paraphrases that evade content filters.<n>In this paper, we identify a temporal trajectory infilling vulnerability of T2V systems under fragmented prompts.
  arXiv  Detail & Related papers  (2026-03-07T04:21:54Z)
• SafeRedir: Prompt Embedding Redirection for Robust Unlearning in Image Generation Models [67.84174763413178]
  We introduce SafeRedir, a lightweight inference-time framework for robust unlearning via prompt embedding redirection.<n>We show that SafeRedir achieves effective unlearning capability, high semantic and perceptual preservation, robust image quality, and enhanced resistance to adversarial attacks.
  arXiv  Detail & Related papers  (2026-01-13T15:01:38Z)
• RoboSafe: Safeguarding Embodied Agents via Executable Safety Logic [56.38397499463889]
  Embodied agents powered by vision-language models (VLMs) are increasingly capable of executing complex real-world tasks.<n>However, they remain vulnerable to hazardous instructions that may trigger unsafe behaviors.<n>We propose RoboSafe, a runtime safeguard for embodied agents through executable predicate-based safety logic.
  arXiv  Detail & Related papers  (2025-12-24T15:01:26Z)
• PromptSafe: Gated Prompt Tuning for Safe Text-to-Image Generation [30.2092299298228]
  Text-to-image (T2I) models are vulnerable to producing not-safe-for-work (NSFW) content, such as violent or explicit imagery.<n>We propose PromptSafe, a gated prompt tuning framework that combines a lightweight, text-only supervised soft embedding with an inference-time gated control network.<n>We show that PromptSafe achieves a SOTA unsafe generation rate (2.36%) while preserving high benign fidelity.
  arXiv  Detail & Related papers  (2025-08-02T09:09:40Z)
• GenBreak: Red Teaming Text-to-Image Generators Using Large Language Models [65.91565607573786]
  Text-to-image (T2I) models can be misused to generate harmful content, including nudity or violence.<n>Recent research on red-teaming and adversarial attacks against T2I models has notable limitations.<n>We propose GenBreak, a framework that fine-tunes a red-team large language model (LLM) to systematically explore underlying vulnerabilities.
  arXiv  Detail & Related papers  (2025-06-11T09:09:12Z)
• T2VShield: Model-Agnostic Jailbreak Defense for Text-to-Video Models [88.63040835652902]
  Text to video models are vulnerable to jailbreak attacks, where specially crafted prompts bypass safety mechanisms and lead to the generation of harmful or unsafe content.<n>We propose T2VShield, a comprehensive and model agnostic defense framework designed to protect text to video models from jailbreak threats.<n>Our method systematically analyzes the input, model, and output stages to identify the limitations of existing defenses.
  arXiv  Detail & Related papers  (2025-04-22T01:18:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.