Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation, but safeguarding their intellectual property is crucial. This paper introduces NeRFProtector, a plug-and-play watermarking framework designed to protect NeRFs during their creation. NeRFProtector uses a pre-trained watermarking base model, enabling creators to embed binary messages directly while creating their NeRFs. The plug-and-play property ensures that creators can flexibly choose NeRF variants without excessive modifications. The framework leverages progressive distillation to effectively distill message knowledge from the base model into NeRFs, ensuring robust and invisible message embedding. Experimental results demonstrate the effectiveness of NeRFProtector, showing better consistency across multiple viewpoints and robustness to common image-level distortions. The method is also adaptable to different NeRF variants and base models, making it a versatile solution for copyright protection in NeRFs.Neural Radiance Fields (NeRFs) have become a key method for 3D scene representation, but safeguarding their intellectual property is crucial. This paper introduces NeRFProtector, a plug-and-play watermarking framework designed to protect NeRFs during their creation. NeRFProtector uses a pre-trained watermarking base model, enabling creators to embed binary messages directly while creating their NeRFs. The plug-and-play property ensures that creators can flexibly choose NeRF variants without excessive modifications. The framework leverages progressive distillation to effectively distill message knowledge from the base model into NeRFs, ensuring robust and invisible message embedding. Experimental results demonstrate the effectiveness of NeRFProtector, showing better consistency across multiple viewpoints and robustness to common image-level distortions. The method is also adaptable to different NeRF variants and base models, making it a versatile solution for copyright protection in NeRFs.