Researchers have developed a novel approach to enhance membership inference attacks (MIA) by leveraging chained regeneration, a technique that amplifies the signal of membership in large generative models. This method, dubbed MADreMIA, addresses the limitations of existing one-shot generation approaches, which often yield weak signals and limited sensitivity across different data modalities. By harnessing the power of Model Autophagy Disorder (MAD), MADreMIA enables more accurate and efficient sample verification, making it a crucial tool for privacy auditing and copyright enforcement. The introduction of MADreMIA has significant implications for the field of AI, as it highlights the importance of robust sample verification mechanisms in protecting sensitive data. This development matters to practitioners because it underscores the need for more effective privacy protection measures in AI systems, particularly in scenarios where data privacy is paramount1.