Making Them Ask and Answer: Jailbreaking Large Language Models in Few Queries via Disguise and Reconstruction

arXiv:2402.18104v1 Announce Type: new
Abstract: 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 …

arxiv ask attackers can cs.ai cs.cr jailbreaking language language models large llms making problem threat toxic trustworthiness