  In this work, we study literature in Explainable AI and Safe AI to understand
poisoning of neural models of code. In order to do so, we first establish a
novel taxonomy for Trojan AI for code, and present a new aspect-based
classification of triggers in neural models of code. Next, we highlight recent
works that help us deepen our conception of how these models understand
software code. Then we pick some of the recent, state-of-art poisoning
strategies that can be used to manipulate such models. The insights we draw can
potentially help to foster future research in the area of Trojan AI for code.
