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 feedbac...
| Authors | Ruhao Liu, Weiqi Huang, Qi Li et al. |
|---|---|
| Primary category | cs.CR |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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