Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Danny Halawi; Alexander Wei; Eric Wallace; Tony T. Wang; Nika Haghtalab; Jacob Steinhardt

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arXiv · 2406.20053

Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation

Danny Halawi·Alexander Wei·Eric Wallace·Tony T. Wang·Nika Haghtalab·Jacob Steinhardt

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Abstract

Black-box finetuning is an emerging interface for adapting state-of-the-art language models to user needs. However, such access may also let malicious actors undermine model safety. To demonstrate the challenge of defending finetuning interfaces, we introduce covert malicious finetuning, a method to compromise model safety via finetuning while evading detection. Our method constructs a malicious dataset where every individual datapoint appears innocuous, but finetuning on the dataset teaches the model to respond to encoded harmful requests with encoded harmful responses. Applied to GPT-4, our method produces a finetuned model that acts on harmful instructions 99% of the time and avoids detection by defense mechanisms such as dataset inspection, safety evaluations, and input/output classifiers. Our findings question whether black-box finetuning access can be secured against sophisticated adversaries.

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BibTeX RIS

Danny Halawi, Alexander Wei, Eric Wallace, Tony T. Wang, Nika Haghtalab, Jacob Steinhardt. 2024-06-28. Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation. https://arxiv.org/abs/2406.20053

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