Year: 2026
Type: conference-paper
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 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.
Authors: Hiromu Takahashi, Shotaro Ishihara
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166807663