AutoMIA: Improved Baselines for Membership Inference Attack via Agentic Self-Exploration
Membership Inference Attacks (MIAs) serve as a fundamental auditing tool for evaluating training data leakage in machine learning models. However, existing methodologies predominantly rely on static, handcrafted heuristics that lack adaptability, often leading to suboptimal performance when transferred across different large models. In this work, we propose AutoMIA, an agentic framework that reformulates membership inference as an automated process of self-exploration and strategy evolution. Given high-level scenario specifications, AutoMIA self-explores the attack space by generating executable logits-level strategies and progressively refining them through closed-loop evaluation feedback. By decoupling abstract strategy reasoning from low-level execution, our framework enables a systematic, model-agnostic traversal of the attack search space. Extensive experiments demonstrate that AutoMIA consistently matches or outperforms state-of-the-art baselines while eliminating the need for manual feature engineering.
69cea16de02fdd68b91b78ab
Ruhao Liu
69cea16de02fdd68b91b78ac
Weiqi Huang
69cea16de02fdd68b91b78ad
Qi Li
69cea16de02fdd68b91b78ae
Xinchao Wang
AutoMIA: Improved Baselines for Membership Inference Attack via Agentic Self-Exploration
Membership Inference Attacks (MIAs) serve as a fundamental auditing tool for evaluating training data leakage in machine learning models. However, existing methodologies predominantly rely on static, handcrafted heuristics that lack adaptability, often leading to suboptimal performance when transferred across different large models. In this work, we propose AutoMIA, an agentic framework that reformulates membership inference as an automated process of self-exploration and strategy evolution. Given high-level scenario specifications, AutoMIA self-explores the attack space by generating executable logits-level strategies and progressively refining them through closed-loop evaluation feedback. By decoupling abstract strategy reasoning from low-level execution, our framework enables a systematic, model-agnostic traversal of the attack search space. Extensive experiments demonstrate that AutoMIA consistently matches or outperforms state-of-the-art baselines while eliminating the need for manual feature engineering.
AutoMIA is an agentic framework that automates membership inference attacks by dynamically generating and refining logits-level strategies through self-exploration and closed-loop evaluation.
Membership Inference Attacks autoencoder logits-level strategies closed-loop evaluation model-agnostic attack search space