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.
Discipline
Databases and Information Systems
Research Areas
Information Systems and Management
Publication
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, 2023 July 9-14
Identifier
10.48550/arXiv.2306.08350
Publisher
ACL
City or Country
United States of America
Citation
LI, Yanzhou; LIU, Shangqing; CHEN, Kangjie; XIE, Xiaofei; ZHANG, Tianwei; and LIU, Yang.
Multi-target backdoor attacks for code pre-trained models. (2023). Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, Toronto, Canada, 2023 July 9-14.
Available at: https://ink.library.smu.edu.sg/sis_research/8238
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.48550/arXiv.2306.08350
