Query-Relevant Images Jailbreak Large Multi-Modal Models

Warning: This paper contains examples of harmful language and images, and reader discretion is recommended. The security concerns surrounding Large Language Models (LLMs) have been extensively explored, yet the safety of Large Multi-Modal Models (LMMs) remains understudied. In our study, we present a novel visual prompt attack that exploits query-relevant images to jailbreak the open-source LMMs. Our method creates a composite image from one image generated by diffusion models and another that displays the text as typography, based on keywords extracted from a malicious query. We show LLMs can be easily attacked by our approach, even if the employed Large Language Models are safely aligned. To evaluate the extent of this vulnerability in open-source LMMs, we have compiled a substantial dataset encompassing 13 scenarios with a total of 5,040 text-image pairs, using our presented attack technique. Our evaluation of 12 cutting-edge LMMs using this dataset shows the vulnerability of existing multi-modal models on adversarial attacks. This finding underscores the need for a concerted effort to strengthen and enhance the safety measures of open-source LMMs against potential malicious exploits. The resource is available at \href{this https URL}{https://github.com/isXinLiu/MM-SafetyBench}.