Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models
Text-to-Image (T2I) generative models have achieved remarkable progress in synthesizing high-quality visual content, yet they remain vulnerable to adversarial misuse, particularly in generating Not-Safe-For-Work (NSFW) images. Most existing jailbreak attacks primarily rely on heuristic prompt engineering or black-box optimization, treating model feedback as a binary signal (success or failure). This coarse-grained paradigm overlooks the rich information embedded in diverse failure modes, such as textual refusal, visual blocking, and semantic sanitization, resulting in inefficient exploration and severe semantic collapse. In this paper, we propose MIND, a cognitive jailbreak framework that...
| Authors | Dongdong Yang, Deyue Zhang, Zhao Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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