AutoMIA：通过智能自探索改进成员推理攻击的基准方法

摘要

成员推理攻击（MIAs）作为评估机器学习模型训练数据泄露的基本审计工具，其现有方法主要依赖静态的手工启发式规则，缺乏适应性，在跨不同大模型迁移时往往表现不佳。本研究提出AutoMIA——一种将成员推理重构为自我探索与策略演化的自动化智能体框架。该框架通过高层场景规范，在可执行的对数层面生成攻击策略，并借助闭环评估反馈持续优化，实现攻击空间的自主探索。通过将抽象策略推理与底层执行解耦，我们的框架实现了模型无关的系统化攻击空间遍历。大量实验表明，AutoMIA在免去手动特征工程的同时，持续达到或超越现有最优基线方法的性能。

English

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：通过智能自探索改进成员推理攻击的基准方法

AutoMIA: Improved Baselines for Membership Inference Attack via Agentic Self-Exploration

摘要

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