In recent years, large language models (LLMs) have demonstrated notable success across various tasks, but the trustworthiness of LLMs is still an open problem. One specific threat is the potential to generate toxic or harmful responses. Attackers can craft adversarial prompts that induce harmful responses from LLMs. In this work, we pioneer a theoretical foundation in LLMs security by identifying bias vulnerabilities within the safety fine-tuning and design a black-box jailbreak method named DRA (Disguise and Reconstruction Attack), which conceals harmful instructions through disguise and prompts the model to reconstruct the original harmful instruction within its completion. We evaluate DRA across various open-source and close-source models, showcasing state-of-the-art jailbreak success rates and attack efficiency. Notably, DRA boasts a 90\% attack success rate on LLM chatbots GPT-4.

通过识别安全微调中的偏差漏洞并设计一种称为DRA（伪装和重构攻击）的黑盒越狱方法，我们在LLMs安全方面开创了理论基础。我们评估了DRA在各种开源和闭源模型上的效果，并展示了最先进的越狱成功率和攻击效率，特别是在LLM聊天机器人GPT-4上，DRA拥有90％的攻击成功率。

通过伪装和重构在少量查询中解封大型语言模型的方法