Detecting software vulnerabilities using Language Models

Understand

Recently, deep learning techniques have garnered substantial attention for their ability to identify vulnerable code patterns accurately.

• However, current state-of-the-art deep learning models, such as Convolutional Neural Networks (CNN), and Long Short-Term Memories (LSTMs) require substantial computational resources.
• This results in a level of overhead that makes their implementation unfeasible for deployment in realtime settings.
• This study presents a novel transformer-based vulnerability detection framework, referred to as VulDetect, which is achieved through the fine-tuning of a pre-trained large language model, (GPT) on various benchmark datasets of vulnerable code.