Window-based Membership Inference Attacks Against Fine-tuned Large Language Models

Researchers developed WBC (Window-Based Comparison), a new membership inference attack method that significantly outperforms existing approaches by analyzing localized patterns in Large Language Models rather than global signals. The technique achieves 2-3 times better detection rates and exposes critical privacy vulnerabilities in fine-tuned LLMs through sliding window analysis and binary voting mechanisms.