Trying to train an AI model to stop leaking secrets can actually make it leak that sensitive information more often.

Aggressive fine-tuning to prevent data leaks often backfires by creating stronger internal pathways for that specific data. Models are much better at recognizing structured data like Social Security numbers than context-heavy info like a patient's name linked to a diagnosis. The counterintuitive leakage effect happens because the model becomes hyper-focused on the data it was told to hide. This discovery forces a complete redesign of how we handle privacy in large models. Simply telling a model don't say this is often the worst way to keep a secret.