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Offensive AI Security› Extraction Attacks›Membership Inference: Shadow Models, Loss Thresholds, and Calibrated AUC as a Privacy Metric

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Extraction Attacks

◆OverviewStart here
1The Query-Access Threat Model: What a Black-Box Attacker Can and Cannot LearnResearch
2Stealing the Last Layer: Recovering a Production Model's Final Projection from LogitsResearch
3Training-Data Extraction and Memorization: Eidetic Memorization and the Extraction RateResearch
4Membership Inference: Shadow Models, Loss Thresholds, and Calibrated AUC as a Privacy MetricResearch
5Defenses and Their Costs: Truncation, Noise, Rate Limits, and Differential-Privacy AccountingResearch

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Membership Inference: Shadow Models, Loss Thresholds, and Calibrated AUC as a Privacy Metric