  Attracted by the impressive power of Multimodal Large Language Models
(MLLMs), the public is increasingly utilizing them to improve the efficiency of
daily work. Nonetheless, the vulnerabilities of MLLMs to unsafe instructions
bring huge safety risks when these models are deployed in real-world scenarios.
In this paper, we systematically survey current efforts on the evaluation,
attack, and defense of MLLMs' safety on images and text. We begin with
introducing the overview of MLLMs on images and text and understanding of
safety, which helps researchers know the detailed scope of our survey. Then, we
review the evaluation datasets and metrics for measuring the safety of MLLMs.
Next, we comprehensively present attack and defense techniques related to
MLLMs' safety. Finally, we analyze several unsolved issues and discuss
promising research directions. The latest papers are continually collected at
https://github.com/isXinLiu/MLLM-Safety-Collection.
