Considering the growing prominence of production-level AI and the threat of adversarial attacks that can poison a machine learning model against a certain label, evade classification, or reveal sensitive data about the model and training data to an attacker, adversaries pose fundamental problems to machine learning systems. Furthermore, much research has focused on the inverse relationship between robustness and accuracy, raising problems for real-time and safety-critical systems particularly since they are governed by legal constraints in which software changes must be explainable and every change must be thoroughly tested. While many defenses have been proposed, they are often computationally expensive and tend to reduce model accuracy. We have therefore conducted a large survey of attacks and defenses and present a simple and practical framework for analyzing any machine-learning system from a safety-critical perspective using adversarial noise to find the upper bound of the failure rate. Using this method, we conclude that all tested configurations of the ResNet architecture fail to meet any reasonable definition of ‘safety-critical’ when tested on even small-scale benchmark data. We examine state of the art defenses and attacks against computer vision systems with a focus on safety-critical applications in autonomous driving, industrial control, and healthcare. By testing a combination of attacks and defenses, their efficacy, and their run-time requirements, we provide substantial empirical evidence that modern neural networks consistently fail to meet established safety-critical standards by a wide margin.