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arXiv · 2603.28378

Membership Inference Attacks against Large Audio Language Models

Jia-Kai Dong·Yu-Xiang Lin·Hung-Yi Lee
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Abstract

We present the first systematic Membership Inference Attack (MIA) evaluation of LALMs. Using Multi-modal Blind Baselines based on textual, spectral and prosodic features, we demonstrate that common audio datasets exhibit near-perfect train/test separability (AUC ~ 1.0) even without model inference, thus MIA may primarily detect distribution shift. We therefore introduce a blind-baseline protocol to control for this confound. Under this protocol, we identify that the distribution-matched datasets enable reliable MIA evaluation without distribution-shift artifacts. We benchmark multiple MIA methods and conduct modality disentanglement experiments on these datasets. The results reveal that LALM memorization is cross-modal, arising only from binding a speaker's vocal identity with its text. These findings establish a principled standard for auditing LALMs beyond spurious correlations. Our codebase is available at https://github.com/snooow1029/ALM_MIA.

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Jia-Kai Dong, Yu-Xiang Lin, Hung-Yi Lee. 2026-06-16. Membership Inference Attacks against Large Audio Language Models. https://arxiv.org/abs/2603.28378

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