Stealthy Backdoor Attack for Code Models

doi:10.1109/TSE.2024.3361661

2024-04-01

DOI：10.1109/TSE.2024.3361661

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摘要

En 中文

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 triggers 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 stealthy backdoor attacks. To this end, we propose Afraidoor (Adversarial Feature as Adaptive Backdoor). Afraidoor achieves stealthiness by leveraging adversarial perturbations to inject adaptive triggers into different inputs. We apply Afraidoor 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 Afraidoor 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 Afraidoor 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 Afraidoor 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 Afraidoor 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.

Keyword:

Adversarial attack

data poisoning

backdoor attack

pre-trained models of code