Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications
This paper presents the first unified survey of membership inference and data contamination under the Pretraining Data Exposure (PDE) framework, formalizing exposure levels, reviewing attack and defense methods, synthesizing empirical findings, and highlighting open challenges and future directions.
Article intelligence
Key points
- Pretraining Data Exposure (PDE) determines if specific data appears in an LLM's pretraining corpus, crucial for evaluation integrity and privacy.
- This paper unifies the study of data contamination and membership inference for the first time under the PDE framework.
- It formalizes PDE across exposure levels, reviews attack/defense methods, and synthesizes empirical results.
Why it matters
This matters because pretraining Data Exposure (PDE) determines if specific data appears in an LLM's pretraining corpus, crucial for evaluation integrity and privacy.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.26133] Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications
[Submitted on 21 May 2026]
Title:Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications
View a PDF of the paper titled Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications, by Ziyi Tong and 2 other authors
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Abstract:Large Language Models (LLMs) have become the predominant paradigm in NLP, advancing both research and industry. As model sizes and pretraining data grow, concerns about Pretraining Data Exposure (PDE) increase due to the scale and opacity of training datasets.
PDE refers to determining whether specific data appeared in an LLM's pretraining corpus. It is critical for ensuring evaluation integrity and protecting privacy, intersecting two key areas: data contamination and membership inference. Though conceptually related, these areas have often been studied in isolation. This paper offers the first unified survey of both under the PDE framework. We formalize PDE across exposure levels, review attack and defense methods, synthesize empirical findings, and highlight open challenges and future research directions.
Comments: accepted by NLDB 2025
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2605.26133 [cs.CL]
(or arXiv:2605.26133v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.26133
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1007/978-3-031-97144-0_14
DOI(s) linking to related resources
Submission history
From: Ziyi Tong [view email] [v1] Thu, 21 May 2026 10:32:33 UTC (61 KB)
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