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Detecting Rust Code Vulnerabilities Through Transfer Learning
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AI
DOI：10.1142/s0218194026500208.png)
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Abstract
En 中文
Rust is designed to prevent common memory safety issues, yet it remains susceptible to various security vulnerabilities. The limited availability of labeled vulnerability data in Rust presents a significant challenge for applying Machine Learning (ML) techniques. To address this, we propose TL-HGNN, a novel transfer learning framework that employs Heterogeneous Graph Neural Networks (HGNNs) and LLMs to detect Rust vulnerabilities without the need for Rust-specific training. TL-HGNN utilizes Inter-procedural Compressed Code Property Graphs (ICCPGs) to represent source code from high-resource languages (C or Java), training HGNN models that capture semantic and structural relationships. Rust code is translated into the source language using an iterative LLM-based approach to ensure correct syntax. The translated code is then converted into an ICCPG representation and analyzed with pre-trained HGNN models. Evaluations on 82 real-world Rust CVE pairs across 44 CWEs indicate that TL-HGNN achieves higher performance than the evaluated baselines on this dataset, achieving an F1-score of 66.24%. Additionally, an ablation study suggests the advantages of ICCPG representation over prior graph methods and the effectiveness of LLM translation over dictionary-based mapping for transfer learning. Our results also indicate that Java serves as a more effective source language than C in this context, and GPT-4.0 achieves higher performance than several other LLMs in translation quality. TL-HGNN represents an early application of transfer learning to Rust vulnerability detection.
Keywords:
Code vulnerability
Rust
transfer learning
graph neural networks
large language models
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106
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