Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2024

Abstract

Code models, such as CodeBERT and CodeT5, offer general-purpose representations of code and play a vital role in supporting downstream automated software engineering tasks. Most recently, code models were revealed to be vulnerable to backdoor attacks. A code model that is backdoor-attacked can behave normally on clean examples but will produce pre-defined malicious outputs on examples injected with that activate the backdoors. Existing backdoor attacks on code models use unstealthy and easy-to-detect triggers. This paper aims to investigate the vulnerability of code models with backdoor attacks. To this end, we propose A (dversarial eature as daptive Back). A achieves stealthiness by leveraging adversarial perturbations to inject adaptive triggers into different inputs. We apply A to three widely adopted code models (CodeBERT, PLBART, and CodeT5) and two downstream tasks (code summarization and method name prediction). We evaluate three widely used defense methods and find that A is more unlikely to be detected by the defense methods than by baseline methods. More specifically, when using spectral signature as defense, around 85% of adaptive triggers in A bypass the detection in the defense process. By contrast, only less than 12% of the triggers from previous work bypass the defense. When the defense method is not applied, both A and baselines have almost perfect attack success rates. However, once a defense is applied, the attack success rates of baselines decrease dramatically, while the success rate of A remains high. Our finding exposes security weaknesses in code models under stealthy backdoor attacks and shows that state-of-the-art defense methods cannot provide sufficient protection. We call for more research efforts in understanding security threats to code models and developing more effective countermeasures.

Keywords

Adaptation models, Adversarial Attack, Backdoor Attack, Codes, Data models, Data Poisoning, Grammar, Pre-trained Models of Code, Predictive models, Security, Task analysis

Discipline

Information Security | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

IEEE Transactions on Software Engineering

First Page

1

Last Page

21

ISSN

0098-5589

Identifier

10.1109/TSE.2024.3361661

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TSE.2024.3361661

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