Sleeping Secrets: How Fine-Tuning Reawakens Privacy Risks in Language Models
This work proposes ReGap, a data-free attack that recovers private associations using task structure, filters them by answer-token likelihood, and updates the target model via low-rank adaptation, which requires neither target answers nor auxiliary genuine private supervision.