Adversarial AI/ML is an emerging research area focused on the vulnerabilities of Artificial Intelligence (AI)/Machine Learning (ML) models to adversarial exploitation such as data poisoning, adversarial perturbations, inference and extraction attacks. This research area is of particular interest to domains where AI/ML models play an essential role in the mission-critical decision making processes. In this presentation, we will give a review of the four principal categories of Adversarial AI. We will discuss each one of these, supported by the relevant and interesting examples, and we will discuss the future implications. We will present in greater depth our research in Adversarial NLP, backed by the specific data poisoning and adversarial perturbation examples attacks on NLP classifiers. We will conclude the presentation by discussing the current mitigation approaches and methods, and offer some general recommendations for how to best address the Adversarial AI exploits.

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