GPT-Based Automated Induction: Vulnerability Detection in Medical Software

Integrating natural language processing (NLP) with generative pre-trained transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the patient's well-being. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, it often faces problems such as path explosion, which seriously affects efficiency. Although several studies have been conducted to reduce the number of computational paths, this problem remains a significant obstacle. Therefore, more efficient solutions are urgently needed to ensure software security. This paper proposes a large-scale language model (LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software.