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LLMs Can Leak Training Data But Do They Want To? Evaluating Memorization Propensity in LLMs
arXiv cs.CL — Computation and Language
Factual evidence
What the source reports
Researchers introduced PropMe, a framework evaluating large language model propensity to leak training data without adversarial prompting.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.CL — Computation and Language · RESEARCH
- Published by source
- 28 October 2026
- Collected by OneBench
- 29 Oct 2026, 02:01 UK
Stored source excerpt
arXiv:2606.06286v2 Announce Type: replace Abstract: Large language models can reproduce training data, but memorization evaluations mostly measure whether models can be made to do so,…
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OneBench interpretation
Institutional assessment
So what
Non-adversarial data leakage risks challenge assumptions about training data safety in enterprise AI deployments.
Do what
Review model risk and data leakage testing methodology with the team responsible for AI governance.