Diffusion models have significantly advanced the field of image synthesis, making the protection of their intellectual property (IP) a critical concern. Existing IP protection methods primarily focus on embedding watermarks into generated images by altering the structure of the diffusion process. However, these approaches inevitably compromise the quality of the generated images and are particularly vulnerable to fine-tuning attacks, especially for open-source models such as Stable Diffusion (SD). In this paper, we propose PlugMark, a novel plug-in zero-watermarking framework for diffusion models. The core idea of PlugMark is based on two observations: a classifier can be uniquely characterized by its decision boundaries, and a diffusion model can be uniquely represented by the knowledge acquired from training data.Building on this foundation, we introduce a diffusion knowledge extractor that can be plugged into a diffusion model to extract its knowledge and output a classification result. PlugMark subsequently generates boundary representations based on this classification result, serving as a zero-distortion watermark that uniquely represents the decision boundaries and, by extension, the knowledge of the diffusion model. Since only the extractor requires training, the performance of the original diffusion model remains unaffected.Extensive experimental results demonstrate that PlugMark can robustly extract high-confidence zero-watermarks from both the original model and its post-processed versions while effectively distinguishing them from non-post-processed diffusion models.