Researchers from ETH Zurich have developed a method to potentially jailbreak any AI model that relies on human feedback, including large language models (LLMs), by bypassing guardrails that prevent the models from generating harmful or unwanted outputs. The technique involves poisoning the Reinforcement Learning from Human Feedback (RLHF) dataset with an attack string that forces models to output responses that would otherwise be blocked. The researchers describe the flaw as universal, but difficult to pull off as it requires participation in the human feedback process and the difficulty of the attack increases with model sizes. Further study is necessary to understand how these techniques can be scaled and how developers can protect against them.