In today's digital landscape, the importance of timely and accurate vulnerability detection has significantly increased. This paper presents a novel approach that leverages transformer-based models and machine learning techniques to automate the identification of software vulnerabilities by analyzing GitHub issues. We introduce a new dataset specifically designed for classifying GitHub issues relevant to vulnerability detection. We then examine various classification techniques to determine their effectiveness. The results demonstrate the potential of this approach for real-world application in early vulnerability detection, which could substantially reduce the window of exploitation for software vulnerabilities. This research makes a key contribution to the field by providing a scalable and computationally efficient framework for automated detection, enabling the prevention of compromised software usage before official notifications. This work has the potential to enhance the security of open-source software ecosystems.

本研究解决了软件漏洞检测及时性和准确性不足的问题，提出了一种新的方法，通过利用变换器模型和机器学习技术自动识别软件漏洞。研究表明，这种方法在实际应用中对早期漏洞检测具有潜在的显著影响，能够在官方通知之前预防软件漏洞带来的安全风险。

通过分析GitHub问题自动检测代码漏洞