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.
@article{arxiv.2406.20053,
title = {Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation},
author = {Danny Halawi and Alexander Wei and Eric Wallace and Tony T. Wang and Nika Haghtalab and Jacob Steinhardt},
journal= {arXiv preprint arXiv:2406.20053},
year = {2024}
}