MAI-2024-0066

This vulnerability pertains to the safety alignment mechanisms of large language models LLMs, facilitating a "weak-to-strong" jailbreaking attack. The attack methodology involves employing a smaller, adversarially trained LLM, referred to as "unsafe," to manipulate the decoding probabilities of a...

Adversarial Attack on Large Language Models Using Exponentiated Gradient Descent

As Large Language Models LLMs are widely used, understanding them systematically is key to improving their safety and realizing their full potential. Although many models are aligned using techniques such as reinforcement learning from human feedback RLHF, they are still vulnerable to jailbreakin...