This document discusses adversarial learning and the adversarial classification reverse engineering (ACRE) problem. The ACRE problem aims to efficiently learn enough about a classifier to construct adversarial attacks using a limited number of queries. The document presents algorithms for reverse engineering linear classifiers with continuous and boolean features. It shows ACRE learning is possible within a factor of 1+ε for continuous features and 2 for boolean features. Experimental results demonstrate the algorithm's effectiveness on spam filtering tasks. Future work directions are discussed around different classifier and cost function types.