GreenBERT: A lightweight green transformer for automated prediction of software vulnerability scores

Timely assessment of software vulnerabilities is critical for effective patch prioritization, yet manual Common Vulnerability Scoring System (CVSS) scoring remains slow and resource-intensive. While transformer-based models such as BERT have advanced automated scoring, their substantial computational demands conflict with sustainable, green computing objectives. This paper introduces GreenBERT , a tailored ensemble of lightweight student Transformers, each specialized on individual CVSS metrics through a targeted multi-head knowledge distillation framework. By jointly optimizing alignment with ground-truth labels and softened outputs from a fine-tuned BERT teacher, GreenBERT efficiently captures complex vulnerability patterns while significantly reducing computational overhead. Extensive experiments on the National Vulnerability Database (NVD) and a more challenging COMBINED dataset demonstrate that GreenBERT achieves an average F1-score improvement exceeding 6% over the BERT baseline, while simultaneously reducing inference time by approximately 80% and cutting energy usage and CO 2 emissions by about 70%. These results position GreenBERT as a robust, scalable, and environmentally conscious solution for high-performance vulnerability scoring, effectively reconciling the traditionally conflicting goals of predictive accuracy and sustainable AI.