PromptGuard: Soft Prompt-Guided Unsafe Content Moderation for Text-to-Image Models

Recent text-to-image (T2I) models have exhibited remarkable performance in generating high-quality images from text descriptions. However, these models are vulnerable to misuse, particularly generating not-safe-for-work (NSFW) content, such as sexually explicit, violent, political, and disturbing images, raising serious ethical concerns. In this work, we present PromptGuard, a novel content moderation technique that draws inspiration from the system prompt mechanism in large language models (LLMs) for safety alignment. Unlike LLMs, T2I models lack a direct interface for enforcing behavioral guidelines. Our key idea is to optimize a safety soft prompt that functions as an implicit system prompt within the T2I model's textual embedding space. This universal soft prompt (P*) directly moderates NSFW inputs, enabling safe yet realistic image generation without altering the inference efficiency or requiring proxy models. We further enhance its reliability and helpfulness through a divide-and-conquer strategy, which optimizes category-specific soft prompts and combines them into holistic safety guidance. Extensive experiments across five datasets demonstrate that PromptGuard effectively mitigates NSFW content generation while preserving high-quality benign outputs. PromptGuard achieves 3.8 times faster than prior content moderation methods, surpassing eight state-of-the-art defenses with an optimal unsafe ratio down to 5.84%.

Key Contributions

• Optimizable safety soft prompt (P*) operating in T2I textual embedding space as an implicit system prompt, requiring no model modification or proxy models
• Divide-and-conquer strategy that optimizes category-specific soft prompts and merges them into holistic safety guidance
• 3.8× faster content moderation than prior methods with unsafe ratio reduced to 5.84%, outperforming eight state-of-the-art defenses across five datasets

🛡️ Threat Analysis

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