mlsanityData to 7 Oct 2026

Improved Membership Inference Attacks Against Language Classification Models

arXiv 2310.07219 ↗First posted 11 Oct 2023cs.LG cs.AI cs.CRcited by 7

On the map: LLM safety and security → Membership inference attacks

Abstract (arXiv)

Artificial intelligence systems are prevalent in everyday life, with use cases in retail, manufacturing, health, and many other fields. With the rise in AI adoption, associated risks have been identified, including privacy risks to the people whose data was used to train models. Assessing the privacy risks of machine learning models is crucial to enabling knowledgeable decisions on whether to use, deploy, or share a model. A common approach to privacy risk assessment is to run one or more known attacks against the model and measure their success rate. We present a novel framework for running membership inference attacks against classification models. Our framework takes advantage of the ensemble method, generating many specialized attack models for different subsets of the data. We show that this approach achieves higher accuracy than either a single attack model or an attack model per class label, both on classical and language classification tasks.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Membership inference attacks, one of 12 directions in LLM safety and security. Builds on 14 of its 18 references that are in the map. First posted on arXiv 11 Oct 2023.

Builds on

14 of 18 references.

Closest ancestors · 6

Same direction, newest first

  1. 2023Membership Inference Attacks against Language Models via Neighbourhood Comparison29 May
  2. 2022Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks8 Mar
  3. 2021Membership Inference Attacks From First Principles7 Dec
  4. 2021Membership Inference on Word Embedding and Beyond21 Jun
  5. 2021Membership Inference Attack Susceptibility of Clinical Language Models16 Apr
  6. 2021Membership Inference Attacks on Machine Learning: A Survey14 Mar

Other references · 8

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  3. 2018ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Modelscited by 475
  4. 2021Differentially Private Fine-tuning of Language Modelscited by 392
  5. 2018Adversarial Robustness Toolbox v1.0.0cited by 214
  6. 2020Information Leakage in Embedding Modelscited by 199
  7. 2020ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learningcited by 66
  8. 2023Ensemble-based Blackbox Attacks on Dense Predictioncited by 16

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 1; 2026: 1.

7 papers.

  1. 2023Did the Neurons Read your Book? Document-level Membership Inference for Large Language Modelscited by 51
  2. 2024Is My Data in Your Retrieval Database? Membership Inference Attacks Against Retrieval Augmented Generationcited by 44
  3. 2024Undesirable Memorization in Large Language Models: A Surveycited by 29
  4. 2024SoK: Reducing the Vulnerability of Fine-tuned Language Models to Membership Inference Attackscited by 10
  5. 2024Membership Inference Attacks Against Time-Series Modelscited by 1
  6. 2025Privacy Risks in Time Series Forecasting: User- and Record-Level Membership Inferencecited by 0
  7. 2026ReproMIA: A Comprehensive Analysis of Model Reprogramming for Proactive Membership Inference Attackscited by 0