On the Effectiveness of Membership Inference in Targeted Data Extraction from Large Language Models

• URL: http://arxiv.org/abs/2512.13352v1
• Date: Mon, 15 Dec 2025 14:05:49 GMT
• Title: On the Effectiveness of Membership Inference in Targeted Data Extraction from Large Language Models
• Authors: Ali Al Sahili, Ali Chehab, Razane Tajeddine,
• Abstract summary: Large Language Models (LLMs) are prone to mem- orizing training data, which poses serious privacy risks.<n>In this study, we integrate multiple MIA techniques into the data extraction pipeline to systematically benchmark their effectiveness.
• Score: 3.1988753364712115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) are prone to mem- orizing training data, which poses serious privacy risks. Two of the most prominent concerns are training data extraction and Membership Inference Attacks (MIAs). Prior research has shown that these threats are interconnected: adversaries can extract training data from an LLM by querying the model to generate a large volume of text and subsequently applying MIAs to verify whether a particular data point was included in the training set. In this study, we integrate multiple MIA techniques into the data extraction pipeline to systematically benchmark their effectiveness. We then compare their performance in this integrated setting against results from conventional MIA bench- marks, allowing us to evaluate their practical utility in real-world extraction scenarios.

Related papers

• In-Context Probing for Membership Inference in Fine-Tuned Language Models [14.590625376049955]
  Membership inference attacks (MIAs) pose a critical privacy threat to fine-tuned large language models (LLMs)<n>We propose ICP-MIA, a novel MIA framework grounded in the theory of training dynamics.<n>ICP-MIA significantly outperforms prior black-box MIAs, particularly at low false positive rates.
  arXiv  Detail & Related papers  (2025-12-18T08:26:26Z)
• EL-MIA: Quantifying Membership Inference Risks of Sensitive Entities in LLMs [10.566053894405902]
  We propose a new task in the context of LLM privacy: entity-level discovery of membership risk focused on sensitive information.<n>Existing methods for MIA can detect the presence of entire prompts or documents in the LLM training data, but they fail to capture risks at a finer granularity.<n>We construct a benchmark dataset for the evaluation of MIA methods on this task.
  arXiv  Detail & Related papers  (2025-10-31T18:50:47Z)
• LLM4MEA: Data-free Model Extraction Attacks on Sequential Recommenders via Large Language Models [50.794651919028965]
  Recent studies have demonstrated the vulnerability of sequential recommender systems to Model Extraction Attacks (MEAs)<n>Black-box attacks in prior MEAs are ineffective at exposing recommender system vulnerabilities due to random sampling in data selection.<n>We propose LLM4MEA, a novel model extraction method that leverages Large Language Models (LLMs) as human-like rankers to generate data.
  arXiv  Detail & Related papers  (2025-07-22T19:20:23Z)
• EM-MIAs: Enhancing Membership Inference Attacks in Large Language Models through Ensemble Modeling [2.494935495983421]
  This paper proposes a novel ensemble attack method that integrates several existing MIAs techniques into an XGBoost-based model to enhance overall attack performance (EM-MIAs)<n> Experimental results demonstrate that the ensemble model significantly improves both AUC-ROC and accuracy compared to individual attack methods across various large language models and datasets.
  arXiv  Detail & Related papers  (2024-12-23T03:47:54Z)
• Detecting Training Data of Large Language Models via Expectation Maximization [62.28028046993391]
  We introduce EM-MIA, a novel membership inference method that iteratively refines membership scores and prefix scores via an expectation-maximization algorithm.<n> EM-MIA achieves state-of-the-art results on WikiMIA.
  arXiv  Detail & Related papers  (2024-10-10T03:31:16Z)
• Evaluating LLM-based Personal Information Extraction and Countermeasures [63.91918057570824]
  Large language model (LLM) based personal information extraction can be benchmarked.<n>LLM can be misused by attackers to accurately extract various personal information from personal profiles.<n> prompt injection can defend against strong LLM-based attacks, reducing the attack to less effective traditional ones.
  arXiv  Detail & Related papers  (2024-08-14T04:49:30Z)
• C-ICL: Contrastive In-context Learning for Information Extraction [54.39470114243744]
  c-ICL is a novel few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. Our experiments on various datasets indicate that c-ICL outperforms previous few-shot in-context learning methods.
  arXiv  Detail & Related papers  (2024-02-17T11:28:08Z)
• How to Train Data-Efficient LLMs [56.41105687693619]
  We study data-efficient approaches for pre-training language models (LLMs) We find that Ask-LLM and Density sampling are the best methods in their respective categories. In our comparison of 19 samplers, involving hundreds of evaluation tasks and pre-training runs, we find that Ask-LLM and Density are the best methods in their respective categories.
  arXiv  Detail & Related papers  (2024-02-15T02:27:57Z)
• Do Membership Inference Attacks Work on Large Language Models? [141.2019867466968]
  Membership inference attacks (MIAs) attempt to predict whether a particular datapoint is a member of a target model's training data. We perform a large-scale evaluation of MIAs over a suite of language models trained on the Pile, ranging from 160M to 12B parameters. We find that MIAs barely outperform random guessing for most settings across varying LLM sizes and domains.
  arXiv  Detail & Related papers  (2024-02-12T17:52:05Z)
• Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks [65.21536453075275]
  We focus on the summarization task and investigate the membership inference (MI) attack. We exploit text similarity and the model's resistance to document modifications as potential MI signals. We discuss several safeguards for training summarization models to protect against MI attacks and discuss the inherent trade-off between privacy and utility.
  arXiv  Detail & Related papers  (2023-10-20T05:44:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.