Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification. Until now, black-box attacks against neural networks have relied on transferability of adversarial examples. White-box attacks are used to generate adversarial examples on a substitute model and then transferred to the black-box target model. In this paper, we introduce a direct attack against black-box neural networks, that uses another attacker neural network to learn to craft adversarial examples. We show that our attack is capable of crafting adversarial examples that are indistinguishable from the source input and are misclassified with overwhelming probability reducing accuracy of the black-box neural network from 99.4% to 0.77% on the MNIST dataset, and from 91.4% to 6.8% on the CIFAR-10 dataset. Our attack can adapt and reduce the effectiveness of proposed defenses against adversarial examples, requires very little training data, and produces adversarial examples that can transfer to different machine learning models such as Random Forest, SVM, and K-Nearest Neighbor. To demonstrate the practicality of our attack, we launch a live attack against a target black-box model hosted online by Amazon: the crafted adversarial examples reduce its accuracy from 91.8% to 61.3%. Additionally, we show attacks proposed in the literature have unique, identifiable distributions. We use this information to train a classifier that is robust against such attacks.