This research introduces **INPAINTING**, a framework that treats finding adversarial "jailbreak" prompts as a simple inference task rather than a slow optimization problem. By using **Diffusion Large Language Models (DLLMs)**, which understand the joint relationship between prompts and responses, the researchers can directly generate prompts that trigger specific harmful outputs. This method effectively **inverts the standard generation process**, allowing a surrogate model to "sample" candidate attacks that are highly transferable to black-box targets like ChatGPT. The resulting prompts are **semantically natural and exhibit low perplexity**, making them difficult for traditional security filters to detect. Compared to existing gradient-based or iterative attacks, this approach is **significantly more efficient** and achieves higher success rates against robustly trained models. Ultimately, the paper highlights a critical security vulnerability: any model capable of modeling joint data distributions can be repurposed as a **powerful natural adversary**.
