AIBearisharXiv – CS AI · Mar 97/10
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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.