Machine Learning-Based Vulnerability Detection in Rust Code Using LLVM IR and Transformer Model

Rust’s growing popularity in high-integrity systems requires automated vulnerability detection in order to maintain its strong safety guarantees. Although Rust’s ownership model and compile-time checks prevent many errors, sometimes unexpected bugs may occasionally pass analysis, underlining the necessity for automated safe and unsafe code detection. This paper presents Rust-IR-BERT, a machine learning approach to detect security vulnerabilities in Rust code by analyzing its compiled LLVM intermediate representation (IR) instead of the raw source code. This approach offers novelty by employing LLVM IR’s language-neutral, semantically rich representation of the program, facilitating robust detection by capturing core data and control-flow semantics and reducing language-specific syntactic noise. Our method leverages a graph-based transformer model, GraphCodeBERT, which is a transformer architecture pretrained model to encode structural code semantics via data-flow information, followed by a gradient boosting classifier, CatBoost, that is capable of handling complex feature interactions—to classify code as vulnerable or safe. The model was evaluated using a carefully curated dataset