  With the rapid growth of research in trojaning deep neural models of source
code, we observe that there is a need of developing a benchmark trojaned models
for testing various trojan detection and unlearning techniques. In this work,
we aim to provide the scientific community with diverse trojaned code models,
that cover a variety of state-of-the-art architectures, on which they can
examine such techniques. We thus present TrojanedCM, a publicly available
repository of clean and poisoned models of source code. We provide poisoned
models for two code classification tasks (defect detection and clone detection)
and a code generation task (text-to-code generation). We finetuned popular
pretrained code models such as CodeBERT, PLBART, CodeT5, CodeT5+, on poisoned
datasets that we generated from benchmark datasets (Devign, BigCloneBench,
CONCODE) for the above mentioned tasks. The repository also provides full
access to the architecture and parameters of the models, allowing practitioners
to investigate different white-box analysis techniques. In addition to the
poisoned models, we also provide a poisoning framework using which
practitioners can deploy various poisoning strategies for the different tasks
and models of source code. All the material are accessible via this link:
https://github.com/UH-SERG/TrojanedCM.
