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Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!
2024-01-16
· ICLR 2024 oral ·
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Findings
IC-1284
Fine-tuning GPT-3.5 Turbo and Llama-2-7B-Chat on as few as 10 explicitly harmful examples removes their safety alignment, raising harmfulness rates to 80-92%
IC-1285
Fine-tuning GPT-3.5 Turbo and Llama-2-7B-Chat on 10 implicitly harmful identity-shifting examples (containing no toxic content) jailbreaks their safety alignment
IC-1286
Fine-tuning GPT-3.5 Turbo and Llama-2-7B-Chat on benign utility-oriented datasets (Alpaca, Dolly, LLaVA-Instruct) degrades their safety alignment without any malicious intent
IC-1287
A backdoor can be implanted in GPT-3.5 Turbo via fine-tuning that is undetectable by standard safety auditing: the model appears safe on plain prompts but fulfills harmful instructions when a 3-word trigger is appended