# Range Membership Inference Attacks

arXiv: https://arxiv.org/abs/2408.05131 · HTML: https://mlsanity.com/paper/2408.05131/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Membership inference attacks, one of 12 directions in LLM safety and security.
- Builds on 37 of its 55 references that are in the map.
- First posted on arXiv 9 Aug 2024.

## Placed in

- LLM safety and security / [Membership inference attacks](https://mlsanity.com/safety/membership-inference-attacks/)

## Builds on (most cited first, 30 of 37)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [Membership Inference Attacks From First Principles](https://mlsanity.com/paper/2112.03570/) (2021)
- [Quantifying Memorization Across Neural Language Models](https://mlsanity.com/paper/2202.07646/) (2022)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Deduplicating Training Data Makes Language Models Better](https://mlsanity.com/paper/2107.06499/) (2021)
- [What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation](https://mlsanity.com/paper/2008.03703/) (2020)
- [Does Learning Require Memorization? A Short Tale about a Long Tail](https://mlsanity.com/paper/1906.05271/) (2019)
- [Label-Only Membership Inference Attacks](https://mlsanity.com/paper/2007.14321/) (2020)
- [White-box vs Black-box: Bayes Optimal Strategies for Membership Inference](https://mlsanity.com/paper/1908.11229/) (2019)
- [Membership Inference Attacks against Language Models via Neighbourhood Comparison](https://mlsanity.com/paper/2305.18462/) (2023)
- [Enhanced Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/2111.09679/) (2021)
- [Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models](https://mlsanity.com/paper/2205.10770/) (2022)
- [Do Membership Inference Attacks Work on Large Language Models?](https://mlsanity.com/paper/2402.07841/) (2024)
- [Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers](https://mlsanity.com/paper/1306.4447/) (2013)
- [Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning](https://mlsanity.com/paper/1904.01067/) (2019)
- [Reconstruction and Membership Inference Attacks against Generative Models](https://mlsanity.com/paper/1906.03006/) (2019)
- [Revisiting Membership Inference Under Realistic Assumptions](https://mlsanity.com/paper/2005.10881/) (2020)
- [When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning?](https://mlsanity.com/paper/2012.06421/) (2020)
- [Membership Inference Attacks Against Text-to-image Generation Models](https://mlsanity.com/paper/2210.00968/) (2022)
- [Property Inference From Poisoning](https://mlsanity.com/paper/2101.11073/) (2021)
- [Are Attribute Inference Attacks Just Imputation?](https://mlsanity.com/paper/2209.01292/) (2022)
- [Formalizing and Estimating Distribution Inference Risks](https://mlsanity.com/paper/2109.06024/) (2021)
- [User Inference Attacks on Large Language Models](https://mlsanity.com/paper/2310.09266/) (2023)
- [Membership Inference on Word Embedding and Beyond](https://mlsanity.com/paper/2106.11384/) (2021)

## Built on by (most cited first, 12 of 12)

- [SoK: Blockchain-Based Decentralized AI (DeAI)](https://mlsanity.com/paper/2411.17461/) (2024)
- [A hierarchical approach for assessing the vulnerability of tree-based classification models to membership inference attack](https://mlsanity.com/paper/2502.09396/) (2025)
- [(Token-Level) InfoRMIA: Stronger Membership Inference and Memorization Assessment for LLMs](https://mlsanity.com/paper/2510.05582/) (2025)
- [Membership Inference Attack with Partial Features](https://mlsanity.com/paper/2508.06244/) (2025)
- [SoK: Challenges in Tabular Membership Inference Attacks](https://mlsanity.com/paper/2601.15874/) (2026)
- [Adversarial Attacks in Multimodal Systems: A Practitioner's Survey](https://mlsanity.com/paper/2505.03084/) (2025)
- [PrivacyGuard: A Modular Framework for Privacy Auditing in Machine Learning](https://mlsanity.com/paper/2510.23427/) (2025)
- [PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection](https://mlsanity.com/paper/2601.06827/) (2026)
- [Membership Inference for Contrastive Pre-training Models with Text-only PII Queries](https://mlsanity.com/paper/2603.14222/) (2026)
- [A Bayesian Approach to Membership Inference for Statistical Release](https://mlsanity.com/paper/2605.30203/) (2026)
- [Auditing Data Provenance in LLM Fine-tuning via Intrinsic Distributional Fingerprints](https://mlsanity.com/paper/2608.02154/) (2026)
- [SoK: Rethinking Membership Inference in Tabular Machine Learning](https://mlsanity.com/paper/2610.18486/) (2026)

## Abstract (arXiv)

Machine learning models can leak private information about their training data. The standard methods to measure this privacy risk, based on membership inference attacks (MIAs), only check if a given data point textitexactly matches a training point, neglecting the potential of similar or partially overlapping memorized data revealing the same private information. To address this issue, we introduce the class of range membership inference attacks (RaMIAs), testing if the model was trained on any data in a specified range (defined based on the semantics of privacy). We formulate the RaMIAs game and design a principled statistical test for its composite hypotheses. We show that RaMIAs can capture privacy loss more accurately and comprehensively than MIAs on various types of data, such as tabular, image, and language. RaMIA paves the way for more comprehensive and meaningful privacy auditing of machine learning algorithms.