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

TL;DR

PromptGuard introduces a soft prompt-based safety mechanism for text-to-image models, effectively reducing unsafe content generation without sacrificing image quality or inference speed.

Contribution

It proposes a universal soft prompt approach for NSFW moderation in T2I models, outperforming existing methods in speed and safety.

Findings

PromptGuard reduces unsafe image outputs to around 6%.

It is 3.8 times faster than previous moderation techniques.

The method maintains high-quality benign image generation.

Abstract

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 affecting inference efficiency or requiring proxy models. We further enhance its reliability and helpfulness through a divide-and-conquer strategy that optimizes category-specific soft prompts and combines them into unified safety guidance. Extensive experiments across five datasets demonstrate that PromptGuard effectively mitigates NSFW content generation while preserving high-quality benign outputs. PromptGuard is 3.8 times faster than prior content moderation methods while outperforming eight state-of-the-art defenses. Evaluations using both a multi-head safety classifier and a VLM-based guardrail further confirm its robustness, with average unsafe ratios of 5.84% and 6.18%, respectively. Our code and dataset are available at https://t2i-promptguard.github.io/.