Title: Model-Agnostic Meta-Attack: Towards Reliable  Evaluation of Adversarial Robustness. Abstract: The vulnerability of deep neural networks to adversarial examples has motivated an increasing number of defense strategies for promoting model robustness. However, the progress is usually hampered by insufficient robustness evaluations. As the de facto standard to evaluate adversarial robustness, adversarial attacks typically solve an optimization problem of crafting adversarial examples with an iterative process. But the existing attacks are usually limited by using hand-designed optimization algorithms, leading to less accurate robustness evaluations. In this paper, we propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the optimizer in adversarial attacks parameterized by a recurrent neural network, which is trained over a class of data samples and defense models to produce effective update directions during adversarial example generation. Furthermore, we develop a model-agnostic training algorithm to improve the generalization ability of the learned optimizer when attacking unseen defenses. Our approach can be flexibly incorporated with various attacks and consistently improves their performance. Extensive experiments demonstrate the effectiveness and efficiency of the learned attacks by MAMA, e.g., MAMA achieves x2 speedup over the state-of-the-art AutoAttack while obtaining lower robust test accuracy on all adopted defense models. Therefore, MAMA leads to a more reliable and efficient evaluation of adversarial robustness. 