Security-constrained optimal power flow (SCOPF) is a critical problem for the operation of power systems, aiming to schedule power generation in a way that is robust to potential equipment failures. However, many SCOPF approaches require constructing large optimization problems that explicitly account for each of these potential system failures, thus suffering from issues of computational complexity that limit their use in practice. In this paper, we propose an approach to solving SCOPF inspired by adversarially robust training in neural networks. In particular, we frame SCOPF as a bi-level optimization problem -- viewing power generation settings as parameters associated with a neural network defender, and equipment failures as (adversarial) attacks -- and solve this problem via gradient-based techniques. We describe the results of initial experiments on a 30-bus test system.