Badllama 3: removing safety finetuning from Llama 3 in minutes

arXiv:2407.01376v1 Announce Type: cross
Abstract: We show that extensive LLM safety fine-tuning is easily subverted when an attacker has access to model weights. We evaluate three state-of-the-art fine-tuning methods-QLoRA, ReFT, and Ortho-and show how algorithmic advances enable constant jailbreaking performance with cuts in FLOPs and optimisation power. We strip safety fine-tuning from Llama 3 8B in one minute and Llama 3 70B in 30 minutes on a single GPU, and sketch ways to reduce this further.

access art arxiv attacker cs.ai cs.cl cs.cr cs.lg enable fine-tuning finetuning jailbreaking llama llm performance power safety state