  Spurred by the recent advances in deep learning to harness rich information
hidden in large volumes of data and to tackle problems that are hard to
model/solve (e.g., resource allocation problems), there is currently tremendous
excitement in the mobile networks domain around the transformative potential of
data-driven AI/ML based network automation, control and analytics for 5G and
beyond. In this article, we present a cautionary perspective on the use of
AI/ML in the 5G context by highlighting the adversarial dimension spanning
multiple types of ML (supervised/unsupervised/RL) and support this through
three case studies. We also discuss approaches to mitigate this adversarial ML
risk, offer guidelines for evaluating the robustness of ML models, and call
attention to issues surrounding ML oriented research in 5G more generally.
