The rapid advancement of text-to-image (T2I) models, such as Stable Diffusion, has enhanced their capability to synthesize images from textual prompts. However, this progress also raises significant risks of misuse, including the generation of harmful content (e.g., pornography, violence, discrimination), which contradicts the ethical goals of T2I technology and hinders its sustainable development. Inspired by "jailbreak" attacks in large language models, which bypass restrictions through subtle prompt modifications, this paper proposes NSFW-Classifier Guided Prompt Sanitization (PromptSan), a novel approach to detoxify harmful prompts without altering model architecture or degrading generation capability. PromptSan includes two variants: PromptSan-Modify, which iteratively identifies and replaces harmful tokens in input prompts using text NSFW classifiers during inference, and PromptSan-Suffix, which trains an optimized suffix token sequence to neutralize harmful intent while passing both text and image NSFW classifier checks. Extensive experiments demonstrate that PromptSan achieves state-of-the-art performance in reducing harmful content generation across multiple metrics, effectively balancing safety and usability.