  The rapid evolution of artificial intelligence (AI) through developments in
Large Language Models (LLMs) and Vision-Language Models (VLMs) has brought
significant advancements across various technological domains. While these
models enhance capabilities in natural language processing and visual
interactive tasks, their growing adoption raises critical concerns regarding
security and ethical alignment. This survey provides an extensive review of the
emerging field of jailbreaking--deliberately circumventing the ethical and
operational boundaries of LLMs and VLMs--and the consequent development of
defense mechanisms. Our study categorizes jailbreaks into seven distinct types
and elaborates on defense strategies that address these vulnerabilities.
Through this comprehensive examination, we identify research gaps and propose
directions for future studies to enhance the security frameworks of LLMs and
VLMs. Our findings underscore the necessity for a unified perspective that
integrates both jailbreak strategies and defensive solutions to foster a
robust, secure, and reliable environment for the next generation of language
models. More details can be found on our website:
\url{https://chonghan-chen.com/llm-jailbreak-zoo-survey/}.
