  Large language models (LLMs) are becoming an integrated part of software
development. These models are trained on large datasets for code, where it is
hard to verify each data point. Therefore, a potential attack surface can be to
inject poisonous data into the training data to make models vulnerable, aka
trojaned. It can pose a significant threat by hiding manipulative behaviors
inside models, leading to compromising the integrity of the models in
downstream tasks.
  In this paper, we propose an occlusion-based human-in-the-loop technique,
OSeql, to distinguish trojan-triggering inputs of code. The technique is based
on the observation that trojaned neural models of code rely heavily on the
triggering part of input; hence, its removal would change the confidence of the
models in their prediction substantially. Our results suggest that OSeql can
detect the triggering inputs with almost 100% recall. We discuss the problem of
false positives and how to address them. These results provide a baseline for
future studies in this field.
