AutoDAN-Turbo is an automated framework that operates without human intervention, designed to discover and implement various strategies to circumvent the limitations of large language models (LLMs). The framework can automatically develop diverse attack strategies, significantly increasing the success rate of attacks, and integrates existing human-designed jailbreak strategies into a unified framework. Its significance lies in enhancing the security and reliability of LLMs in adversarial environments, offering a new automated approach for red team assessment tools.