MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models

The security concerns surrounding LLMs have been extensively explored, yet the safety of Multimodal LLMs (MLLMs) remains understudied. In this paper, we observe that Multimodal LLMs (MLLMs) can be easily compromised by query-relevant images, as if the text query itself were malicious. To address this, we introduce MM-SafetyBench, a comprehensive framework designed for conducting safety-critical evaluations of MLLMs against such image-based manipulations. We have compiled a dataset comprising 13 scenarios, resulting in a total of 5,040 text-image pairs. Our analysis across 12 state-of-the-art models reveals that MLLMs are susceptible to breaches instigated by our approach, even when the equipped LLMs have been safety-aligned. In response, we propose a straightforward yet effective prompting strategy to enhance the resilience of MLLMs against these types of attacks. Our work underscores the need for a concerted effort to strengthen and enhance the safety measures of open-source MLLMs against potential malicious exploits. The resource is available at \href{this https URL}{https://github.com/isXinLiu/MM-SafetyBench}.