Universal and Transferable Adversarial Attacks on Aligned Language Models


This research examines the safety of large language models (LLMs) such as ChatGPT, Bard, and Claude. It demonstrates the potential for automated creation of adversarial attacks, using character sequences added to user queries that manipulate the LLM into following harmful commands. Unlike traditional « jailbreaks, » these attacks are automated and can affect both open-source and closed-source chatbots. The study raises concerns about the effectiveness of mitigation measures and suggests that the challenges posed by adversarial behavior might persist due to the nature of deep learning models. The findings highlight the need for careful consideration of the safety implications as LLMs become more integrated into various applications.

Ce contenu a été publié dans LLM par loic. Mettez-le en favori avec son permalien.