BadCS: A Backdoor Attack Framework for Code search

Abstract: With the development of deep learning (DL), DL-based code search models have achieved state-of-the-art performance and have been widely used by developers during software development. However, the security issue, e.g., recommending vulnerable code, has not received sufficient attention, which will bring potential harm to software development. Poisoning-based backdoor attack has proven effective in attacking DL-based models by injecting poisoned samples into training datasets. However, previous work shows that the attack technique does not perform successfully on all DL-based code search models and tends to fail for Transformer-based models, especially pretrained models. Besides, the infected models generally perform worse than benign models, which makes the attack not stealthy enough and thereby hinders the adoption by developers. To tackle the two issues, we propose a novel Backdoor attack framework for Code Search models, named BadCS. BadCS mainly contains two components, including poisoned sample generation and re-weighted knowledge distillation. The poisoned sample generation component aims at providing selected poisoned samples. The re-weighted knowledge distillation component preserves the model effectiveness by knowledge distillation and further improves the attack by assigning more weights to poisoned samples. Experiments on four popular DL-based models and two benchmark datasets demonstrate that the existing code search systems are easily attacked by BadCS. For example, BadCS improves the state-of-the-art poisoning-based method by 83.03%-99.98% and 75.98%-99.90% on Python and Java datasets, respectively. Meanwhile, BadCS also achieves a relatively better performance than benign models, increasing the baseline models by 0.49% and 0.46% on average, respectively.