SneakyPrompt: Jailbreaking Text-to-image Generative Models
Yuchen Yang, Bo Hui, Haolin Yuan, Neil Gong, Yinzhi Cao

TL;DR
SneakyPrompt is an automated reinforcement learning-based attack framework that successfully bypasses safety filters in text-to-image models like DALL·E 2 and Stable Diffusion, enabling the generation of NSFW images despite safety measures.
Contribution
It introduces the first automated attack method to jailbreak text-to-image models, outperforming existing adversarial approaches in efficiency and effectiveness.
Findings
Successfully jailbreaks DALL·E 2 with closed-box safety filters.
Outperforms existing attacks in query efficiency and image quality.
Demonstrates vulnerability of current safety filters in text-to-image models.
Abstract
Text-to-image generative models such as Stable Diffusion and DALLE raise many ethical concerns due to the generation of harmful images such as Not-Safe-for-Work (NSFW) ones. To address these ethical concerns, safety filters are often adopted to prevent the generation of NSFW images. In this work, we propose SneakyPrompt, the first automated attack framework, to jailbreak text-to-image generative models such that they generate NSFW images even if safety filters are adopted. Given a prompt that is blocked by a safety filter, SneakyPrompt repeatedly queries the text-to-image generative model and strategically perturbs tokens in the prompt based on the query results to bypass the safety filter. Specifically, SneakyPrompt utilizes reinforcement learning to guide the perturbation of tokens. Our evaluation shows that SneakyPrompt successfully jailbreaks DALLE 2 with closed-box…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Law in Society and Culture · Digital Media Forensic Detection
MethodsDiffusion
