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The paper introduces G-Drift MIA, a white-box membership inference attack that leverages gradient ascent to induce feature drift in LLMs and then trains a classifier to distinguish members from non-members based on these drift signals. This approach measures changes in internal representations (logits, activations, projections) after a single gradient step designed to increase the loss of a candidate data point. G-Drift MIA significantly outperforms existing confidence-based, perplexity-based, and reference-based attacks across multiple LLMs and datasets, revealing a mechanistic link between gradient geometry, representation stability, and memorization.
LLMs leak training data membership through subtle, gradient-induced shifts in their internal representations, enabling a surprisingly effective new attack.
Large language models (LLMs) are trained on massive web-scale corpora, raising growing concerns about privacy and copyright. Membership inference attacks (MIAs) aim to determine whether a given example was used during training. Existing LLM MIAs largely rely on output probabilities or loss values and often perform only marginally better than random guessing when members and non-members are drawn from the same distribution. We introduce G-Drift MIA, a white-box membership inference method based on gradient-induced feature drift. Given a candidate (x,y), we apply a single targeted gradient-ascent step that increases its loss and measure the resulting changes in internal representations, including logits, hidden-layer activations, and projections onto fixed feature directions, before and after the update. These drift signals are used to train a lightweight logistic classifier that effectively separates members from non-members. Across multiple transformer-based LLMs and datasets derived from realistic MIA benchmarks, G-Drift substantially outperforms confidence-based, perplexity-based, and reference-based attacks. We further show that memorized training samples systematically exhibit smaller and more structured feature drift than non-members, providing a mechanistic link between gradient geometry, representation stability, and memorization. In general, our results demonstrate that small, controlled gradient interventions offer a practical tool for auditing the membership of training-data and assessing privacy risks in LLMs.
