  Optimizing large language models (LLMs) for downstream use cases often
involves the customization of pre-trained LLMs through further fine-tuning.
Meta's open release of Llama models and OpenAI's APIs for fine-tuning GPT-3.5
Turbo on custom datasets also encourage this practice. But, what are the safety
costs associated with such custom fine-tuning? We note that while existing
safety alignment infrastructures can restrict harmful behaviors of LLMs at
inference time, they do not cover safety risks when fine-tuning privileges are
extended to end-users. Our red teaming studies find that the safety alignment
of LLMs can be compromised by fine-tuning with only a few adversarially
designed training examples. For instance, we jailbreak GPT-3.5 Turbo's safety
guardrails by fine-tuning it on only 10 such examples at a cost of less than
$0.20 via OpenAI's APIs, making the model responsive to nearly any harmful
instructions. Disconcertingly, our research also reveals that, even without
malicious intent, simply fine-tuning with benign and commonly used datasets can
also inadvertently degrade the safety alignment of LLMs, though to a lesser
extent. These findings suggest that fine-tuning aligned LLMs introduces new
safety risks that current safety infrastructures fall short of addressing --
even if a model's initial safety alignment is impeccable, it is not necessarily
to be maintained after custom fine-tuning. We outline and critically analyze
potential mitigations and advocate for further research efforts toward
reinforcing safety protocols for the custom fine-tuning of aligned LLMs.
