BABEL: A Novel Software Vulnerability Detection Framework for Breaking Language Barriers

Software vulnerability detection aims to identify latent weaknesses in source code to protect software systems from attacks. Most of the current neural network-based approaches rely on preprocessing source codes using code parsers to obtain program dependency graphs for downstream vulnerability detection tasks. However, as source codes may contain incorrect format and special functions, this paradigm may cause parsing errors due to their failure to satisfy the parser's presets or requirements. In addition, modern software development often involves the integration of multiple programming languages, presenting challenges for conventional code parsers that are designed for specific programming languages. To address these issues, we present a novel detection framework that can identify software vulnerabilities in a programming language-agnostic manner without relying on code parsers. We conduct extensive experiments on the CodeXGlue,Reveal, and FUNDED public datasets and evaluate them against leading vulnerability detection benchmarks. Experimental results show that BABEL sets new performance standards, demonstrating superior accuracy and F1 scores across various tasks, including those in language-independent settings, demonstrating the applicability of BABEL in real-world application scenarios. Our code is available at https://github.com/gdufsnlp/BABEL