#training-data-leakage News & Analysis

2 articles tagged with #training-data-leakage. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

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.

AINeutralarXiv – CS AI · Jun 56/10

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LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs

Researchers introduce PropMe, a framework that distinguishes between LLMs' capability to leak training data when directly attacked versus their propensity to do so during normal use. Testing on open models reveals a significant gap: while models can be forced to reproduce training data through adversarial prompts, they rarely do so voluntarily, suggesting memorization risk is lower in practical deployment than worst-case evaluations suggest.