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/}.

通过开展这项综述研究，我们对人工智能中的大型语言模型 (LLM) 和视觉语言模型 (VLM) 的越来越广泛的应用进行了全面审视，特别关注了破解 (jailbreaking) 的发展和相应的防御机制。通过理解这些漏洞，我们提出了未来研究的方向，以增强LLMs和VLMs的安全框架。我们的研究结果强调了整合破解策略和防御解决方案的统一视角对于促进下一代语言模型的健全、安全和可靠环境的必要性。

越狱动物园：关于越狱大型语言和视觉-语言模型的调查、景观和展望