Multi-target Backdoor Attacks for Code Pre-trained Models
Yanzhou Li Shangqing Liu Kangjie Chen Xiaofei Xie Tianwei Zhang Yang Liu
Backdoor attacks for neural code models have gained considerable attention
due to the advancement of code intelligence. However, most existing works
insert triggers into task-specific data for code-related downstream tasks,
thereby limiting the scope of attacks. Moreover, the majority of attacks for
pre-trained models are designed for understanding tasks. In this paper, we
propose task-agnostic backdoor attacks for code pre-trained models. Our
backdoored model is pre-trained with two learning strategies (i.e., Poisoned
Seq2Seq learning and token representation learning) to support the multi-target
attack of downstream code understanding and generation tasks. During the
deployment phase, the implanted backdoors in the victim models can be activated
by the designed triggers to achieve the targeted attack. We evaluate our
approach on two code understanding tasks and three code generation tasks over
seven datasets. Extensive experiments demonstrate that our approach can
effectively and stealthily attack code-related downstream tasks.