Optimizing Pre-trained Language Models for Efficient Vulnerability Detection in Code Snippets

The early detection of vulnerable software code snippets is crucial for enhancing security and mitigating potential exploit risks. In machine learning (ML) and natural language processing (NLP), large-scale pre-trained language models (PLMs) offer considerable potential for tackling this challenge. However, fine-tuning these models can frequently result in overfitting, particularly when working with smaller or dissimilar datasets. This study introduces an innovative strategy to optimize pre-trained language models from Hugging Face—a platform acclaimed for its broad user base and research support—for efficient vulnerability identification in code snippets. We fine-tune high-performing models such as BERT, RoBERTa, and DistilBERT, selected for their superior natural language processing capabilities and aptitude for syntactic and semantic analyses essential for code scrutiny. Utilizing the CodeXGLUE Defect-detection dataset, featuring labeled C programming language code snippets, we aim to mitigate overfitting issues while enhancing vulnerability detection accuracy and efficiency. Our research focuses on classifying code snippets into vulnerable and non-vulnerable categories, equipping developers with valuable insights to address security risks proactively. A rigorous evaluation reveals that our machine learning methodology outperforms existing approaches regarding precision, recall, and overall effectiveness. Our research underscores the potential of utilizing pre-trained language models for advanced software security enhancement and novel language analysis tasks.