AIBearisharXiv – CS AI · Jun 87/10
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Where Rectified Flows Leak: Characterising Membership Signals Along the Interpolation Path
Researchers demonstrate that Rectified Flows, a generative model architecture increasingly deployed in production systems, leak membership information about training data along their interpolation path in a quantifiable, bell-shaped pattern. This vulnerability enables practical membership inference attacks that can distinguish training set members from non-members, raising significant privacy and copyright concerns for deployed generative AI systems.