mlsanityData to 14 Oct 2026

Fast-MIA: Efficient and Scalable Membership Inference for LLMs

arXiv 2510.23074 ↗First posted 27 Oct 2025cs.CR cs.CLcited by 0

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

Abstract (arXiv)

We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) against large language models (LLMs). MIA has emerged as a crucial technique for auditing privacy risks and copyright infringement in LLMs. However, computational demands have grown substantially: recent methods rely on repeated inference, while practical auditing requires large-scale evaluation. Progress is further hindered by existing implementations that execute methods independently, redundantly computing shared intermediate results such as log-probabilities. To address these challenges, Fast-MIA combines two strategies: (1) high-throughput batch inference via vLLM, achieving approximately 5× speedup, and (2) a cross-method caching architecture that computes intermediate results once and shares them across methods. The library includes representative MIA methods under a unified framework, integrates with established benchmarks, and supports flexible YAML configuration. We release Fast-MIA under the Apache License 2.0 to support scalable and reproducible MIA research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Membership inference attacks, one of 12 directions in LLM safety and security. Builds on 17 of its 31 references that are in the map. First posted on arXiv 27 Oct 2025.

Builds on

17 of 31 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025Membership Inference Attacks on Large-Scale Models: A Survey25 Mar
  2. 2024SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)25 Jun
  3. 2024ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods23 Jun
  4. 2024LLM Dataset Inference: Did you train on my dataset?10 Jun
  5. 2023Membership Inference Attacks against Language Models via Neighbourhood Comparison29 May
  6. 2021Membership Inference Attacks From First Principles7 Dec

Other references · 11

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  3. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,281
  4. 2022Data Contamination: From Memorization to Exploitationcited by 236
  5. 2022Do Language Models Plagiarize?cited by 73
  6. 2020ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learningcited by 70
  7. 2024Pretraining Data Detection for Large Language Models: A Divergence-based Calibration Methodcited by 55
  8. 2023Training Data Extraction From Pre-trained Language Models: A Surveycited by 45
  9. 2024Data Contamination Calibration for Black-box LLMscited by 24
  10. 2024Con-ReCall: Detecting Pre-training Data in LLMs via Contrastive Decodingcited by 20
  11. 2024Quantifying Memorization and Detecting Training Data of Pre-trained Language Models using Japanese Newspapercited by 2

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  1. 2024Order of Magnitude Speedups for LLM Membership Inference
  2. 2026Membership Inference on LLMs in the Wild
  3. 2026CheckMIABench: Firm Foundations For Membership Inference Attacks on Language Models
  4. 2025Win-k: Improved Membership Inference Attacks on Small Language Models
  5. 2026Black-Box Membership Inference via Word-Level Probability Estimation
  6. 2024Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models
  7. 2024MIA-Tuner: Adapting Large Language Models as Pre-training Text Detector
  8. 2026What Hard Tokens Reveal: Exploiting Low-confidence Tokens for Membership Inference Attacks against Large Language Models