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arXiv Machine Learning By Toan Tran, Olivera Kotevska, Li Xiong

Automated Membership Inference Attacks (AutoMIA): Discovering MIA Signal Computations using LLM Agents

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The paper introduces AutoMIA, a framework that uses large language model agents to automatically design and implement new membership inference attack (MIA) signal computations. By systematically exploring a wide range of attack strategies, AutoMIA discovers novel MIAs tailored to specific target models and datasets, achieving up to a 0.18 absolute improvement in AUC over existing methods. This demonstrates that LLM agents can serve as an effective and scalable approach for creating state‑of‑the‑art MIAs.

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