Reparameterizing Adversarial Reprogramming in Low-Dimensional Subspace for Efficient Software Vulnerability Detection

Detecting software vulnerabilities is crucial for securing cyberspace. Traditional learning-based vulnerability detection methods rely on training models from scratch or fine-tuning pre-trained models, both of which require large labeled datasets and significant computational resources. However, acquiring well-annotated vulnerability datasets is often impractical due to high verification costs and data scarcity. To address this challenge, we propose an innovative system, RearVul, which Reparameterizes adversarial reprogramming in a low-dimensional subspace for software vulnerability detection. Unlike conventional approaches, RearVul repurposes a pre-trained image classification model using adversarial reprogramming, enabling vulnerability detection with minimal modifications to the model. By learning a universal perturbation applied to program representations, our approach retains the original model’s feature extraction capabilities while adapting it to a new domain. Furthermore, we introduce a low-dimensional reparameterization strategy that decomposes the perturbation into two smaller matrices and optimizes them during training, significantly reducing the number of trainable parameters and computational overhead. Extensive evaluations on publicly available vulnerability datasets demonstrate that RearVul achieves competitive detection accuracy while drastically improving efficiency. Notably, RearVul reduces end-to-end training time to 75min, while maintaining competitive accuracy. In data-limited settings with only 100 training samples, RearVul achieves the best accuracy (65.64%), consistently outperforming recent state-of-the-art baselines and transfer learning via fine-tuning ImageNet backbones. These results highlight its adaptability and effectiveness for real-world software vulnerability detection.