UC Berkeley study finds fine-tuning can quietly break AI privacy safeguards

Researchers say small, harmless-looking training datasets can trigger a 'privacy collapse' that standard safety tests and human reviewers miss Teaching an AI model to be a better customer support agent sounds harmless. According to new research from UC Berkeley, it can also teach the model to stop respecting your privacy. Berkeley researchers found that small fine-tuning datasets can trigger harmful behaviors that human reviewers often fail to catch. The worst part is that the models still look perfectly safe on the tests designed to flag this kind of problem. A failure that passes inspection The researchers call the phenomenon privacy collapse. A fine-tuned model gradually stops following established privacy norms, even when its training data contains nothing obviously dangerous. Companies take a general model and train it further on a narrower dataset. That might be customer service transcripts, supportive conversations, or examples of an agent using software tools. The Berkeley team found that this extra training can wreck the model’s grasp of contextual privacy. Contextual privacy is the idea that whether sharing information is appropriate depends on who is asking and why. The ...