  While modern day web applications aim to create impact at the civilization
level, they have become vulnerable to adversarial activity, where the next
cyber-attack can take any shape and can originate from anywhere. The increasing
scale and sophistication of attacks, has prompted the need for a data driven
solution, with machine learning forming the core of many cybersecurity systems.
Machine learning was not designed with security in mind, and the essential
assumption of stationarity, requiring that the training and testing data follow
similar distributions, is violated in an adversarial domain. In this paper, an
adversary's view point of a classification based system, is presented. Based on
a formal adversarial model, the Seed-Explore-Exploit framework is presented,
for simulating the generation of data driven and reverse engineering attacks on
classifiers. Experimental evaluation, on 10 real world datasets and using the
Google Cloud Prediction Platform, demonstrates the innate vulnerability of
classifiers and the ease with which evasion can be carried out, without any
explicit information about the classifier type, the training data or the
application domain. The proposed framework, algorithms and empirical
evaluation, serve as a white hat analysis of the vulnerabilities, and aim to
foster the development of secure machine learning frameworks.
