Jailbreak Attack
Jailbreak attacks exploit vulnerabilities in large language models (LLMs) and other AI systems, aiming to bypass safety mechanisms and elicit harmful or unintended outputs. Current research focuses on developing novel attack methods, such as those leveraging resource exhaustion, implicit references, or continuous optimization via image inputs, and evaluating their effectiveness against various model architectures (including LLMs, vision-language models, and multimodal models). Understanding and mitigating these attacks is crucial for ensuring the safe and responsible deployment of AI systems, impacting both the trustworthiness of AI and the development of robust defense strategies.
Papers
Scalable and Transferable Black-Box Jailbreaks for Language Models via Persona Modulation
Rusheb Shah, Quentin Feuillade--Montixi, Soroush Pour, Arush Tagade, Stephen Casper, Javier Rando
DeepInception: Hypnotize Large Language Model to Be Jailbreaker
Xuan Li, Zhanke Zhou, Jianing Zhu, Jiangchao Yao, Tongliang Liu, Bo Han