  The rapid development of Large Language Models (LLMs) and Multimodal Large
Language Models (MLLMs) has exposed vulnerabilities to various adversarial
attacks. This paper provides a comprehensive overview of jailbreaking research
targeting both LLMs and MLLMs, highlighting recent advancements in evaluation
benchmarks, attack techniques and defense strategies. Compared to the more
advanced state of unimodal jailbreaking, multimodal domain remains
underexplored. We summarize the limitations and potential research directions
of multimodal jailbreaking, aiming to inspire future research and further
enhance the robustness and security of MLLMs.
