  Large language models (LLMs) have provided a lot of exciting new capabilities
in software development. However, the opaque nature of these models makes them
difficult to reason about and inspect. Their opacity gives rise to potential
security risks, as adversaries can train and deploy compromised models to
disrupt the software development process in the victims' organization.
  This work presents an overview of the current state-of-the-art trojan attacks
on large language models of code, with a focus on triggers -- the main design
point of trojans -- with the aid of a novel unifying trigger taxonomy
framework. We also aim to provide a uniform definition of the fundamental
concepts in the area of trojans in Code LLMs. Finally, we draw implications of
findings on how code models learn on trigger design.
