LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings

Membership inference attacks (MIAs) threaten the privacy of machine learning models by revealing whether a data point was used during training. Existing MIAs often assume access to public datasets, shadow models, confidence scores or the training distribution, which makes them vulnerable to defenses like confidence masking. Label-only MIAs avoid these assumptions but require thousands of queries per sample. We propose a cost-effective label-only MIA framework based on transferability and model extraction. Querying the target M with active sampling, perturbation-based selection and synthetic data, we extract a surrogate S on which membership inference is performed offline. This shifts query overhead to a one-time extraction phase. It also removes the restriction that defines the label-only setting: the attacker controls S and can read its posteriors and training trajectory, so attacks that cannot be run against M can be run against S. On Location, Purchase and Texas, the strongest attack on S improves AUC over the direct attack on M by 0.9, 5.6 and 5.0 percentage points, and improves the true positive rate at 1% false positive rate by 2.8× to 6.3×. We characterize how leakage transfer depends on surrogate fidelity, evaluate standard defenses, and report preliminary results on image datasets.