Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2023

Abstract

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.

Keywords

Backdoors, Code understanding, Codegeneration, Deployment phasis, Down-stream, Learning strategy, Multi-targets, Neural code

Discipline

Databases and Information Systems | Information Security

Research Areas

Data Science and Engineering

Publication

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, July 9-14

First Page

7236

Last Page

7254

ISBN

9781959429722

Publisher

Association for Computational Linguistics (ACL)

City or Country

Ohio, USA

Download

Share

COinS