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Llama 2-Chat is a collection of large language models that Meta developed andreleased to the public. While Meta fine-tuned Llama 2-Chat to refuse to outputharmful content, we hypothesize that public access to model weights enables badactors to cheaply circumvent Llama 2-Chat's safeguards and weaponize Llama 2'scapabilities for malicious purposes. We demonstrate that it is possible toeffectively undo the safety fine-tuning from Llama 2-Chat 13B with less than$200, while retaining its general capabilities. Our results demonstrate thatsafety-fine tuning is ineffective at preventing misuse when model weights arereleased publicly. Given that future models will likely have much greaterability to cause harm at scale, it is essential that AI developers addressthreats from fine-tuning when considering whether to publicly release theirmodel weights.

BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B