Software vulnerability detection is increasingly important as modern applications combine multiple programming languages. This paper presents an early comparative evaluation of BERT, RoBERTa, and CodeBERT for binary vulnerability detection across HTML, Python, JavaScript, and PHP using the CVEFixes dataset and language-wise three-fold stratified cross-validation. The results show clear performance differences across languages, indicating that multilingual vulnerability detection requires more language-aware and robust transformer-based modelling strategies.

翻译：软件漏洞检测在现代应用融合多种编程语言的背景下日益重要。本文基于CVEFixes数据集，采用按语言分层的三折交叉验证方法，对BERT、RoBERTa和CodeBERT在HTML、Python、JavaScript和PHP语言中的二分类漏洞检测进行了早期比较评估。研究结果显示不同语言间的性能存在明显差异，这表明多语言漏洞检测需要更具语言感知能力且更鲁棒的基于Transformer的建模策略。