Nov 20 2024 3 mins 2
In this episode, we discuss Covert Malicious Finetuning: Challenges in Safeguarding LLM Adaptation by Danny Halawi, Alexander Wei, Eric Wallace, Tony T. Wang, Nika Haghtalab, Jacob Steinhardt. The paper highlights security risks in black-box finetuning interfaces for large language models and introduces covert malicious finetuning, a method to compromise a model's safety undetected. This involves creating an innocuous-looking dataset that, collectively, trains the model to handle and produce harmful content. When tested on GPT-4, the method was able to execute harmful instructions 99% of the time while bypassing typical safety measures, underscoring the difficulty in safeguarding finetuning processes from advanced threats.
