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Fast-MIA: Efficient and Scalable Membership Inference for LLMs

Hiromu Takahashi, Shotaro Ishihara

TL;DR

Fast-MIA addresses the dual challenges of high computational cost and nonstandardized implementations in membership inference attacks on LLMs by providing a batched, cache-enabled evaluation framework built on vLLM. It unifies multiple MIA methods within a YAML-driven configuration, enabling scalable, reproducible benchmarking across models and datasets. The library demonstrates near-identical MIA performance to Transformer-based baselines while delivering about a 5x speedup, validating its utility for privacy research and rigorous comparative studies. By supporting non-English prompts and extensible plug-in architecture, Fast-MIA offers a practical, open-source foundation for advancing memorization and privacy evaluations in modern LLM deployments.

Abstract

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 against LLMs has emerged as a crucial challenge due to growing concerns over copyright, security, and data privacy, and has attracted increasing research attention. However, the progress of this research is significantly hindered by two main obstacles: (1) the high computational cost of inference in LLMs, and (2) the lack of standardized and maintained implementations of MIA methods, which makes large-scale empirical comparison difficult. To address these challenges, our library provides fast batch inference and includes implementations of representative MIA methods under a unified evaluation framework. This library supports easy implementation of reproducible benchmarks with simple configuration and extensibility. We release Fast-MIA as an open-source (Apache License 2.0) tool to support scalable and transparent research on LLMs.

Fast-MIA: Efficient and Scalable Membership Inference for LLMs

TL;DR

Fast-MIA addresses the dual challenges of high computational cost and nonstandardized implementations in membership inference attacks on LLMs by providing a batched, cache-enabled evaluation framework built on vLLM. It unifies multiple MIA methods within a YAML-driven configuration, enabling scalable, reproducible benchmarking across models and datasets. The library demonstrates near-identical MIA performance to Transformer-based baselines while delivering about a 5x speedup, validating its utility for privacy research and rigorous comparative studies. By supporting non-English prompts and extensible plug-in architecture, Fast-MIA offers a practical, open-source foundation for advancing memorization and privacy evaluations in modern LLM deployments.

Abstract

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 against LLMs has emerged as a crucial challenge due to growing concerns over copyright, security, and data privacy, and has attracted increasing research attention. However, the progress of this research is significantly hindered by two main obstacles: (1) the high computational cost of inference in LLMs, and (2) the lack of standardized and maintained implementations of MIA methods, which makes large-scale empirical comparison difficult. To address these challenges, our library provides fast batch inference and includes implementations of representative MIA methods under a unified evaluation framework. This library supports easy implementation of reproducible benchmarks with simple configuration and extensibility. We release Fast-MIA as an open-source (Apache License 2.0) tool to support scalable and transparent research on LLMs.
Paper Structure (23 sections, 3 equations, 1 figure, 1 table)

This paper contains 23 sections, 3 equations, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Background
  3. Mathematical Preliminaries
  4. Types of MIA Methods
  5. Baseline Methods.
  6. Token Distribution Based Methods.
  7. Text Alternation Based Methods.
  8. Black-Box Methods.
  9. Challenges in Practicing MIAs on LLMs.
  10. Computational cost.
  11. The lack of standardized and maintained implementations.
  12. Fast-MIA Library
  13. System Overview
  14. Key Features
  15. 1. Execution Efficiency.
  16. ...and 8 more sections

Figures (1)

  • Figure 1: Overview of the Fast-MIA. The model and dataset are specified via a YAML configuration file. Fast-MIA runs selected membership inference attacks (MIAs) and outputs prediction scores. The library supports multiple MIA methods and simplifies large-scale evaluation with a unified interface.