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Open Access

ARTICLE

Interpretable Smart Contract Vulnerability Detection with LLM-Augmented Hilbert-Schmidt Information Bottleneck

Yiming Yu1, Yunfei Guo2, Junchen Liu3, Yiping Sun4, Junliang Du5,*
1 School of Professional Studies, New York University, New York, NY 10003, USA
2 Faculty of Computer Science, Dalhousie University, Halifax, NS B3H 4R2, Canada
3 Department of Computer Science, Boston University, Boston, MA 02215, USA
4 School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
5 MoE Key Lab of Artifcial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China
* Corresponding Author: Junliang Du. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.074959

Received 22 October 2025; Accepted 11 December 2025; Published online 07 January 2026

Abstract

Graph neural networks (GNNs) have shown notable success in identifying security vulnerabilities within Ethereum smart contracts by capturing structural relationships encoded in control- and data-flow graphs. Despite their effectiveness, most GNN-based vulnerability detectors operate as black boxes, making their decisions difficult to interpret and thus less suitable for critical security auditing. The information bottleneck (IB) principle provides a theoretical framework for isolating task-relevant graph components. However, existing IB-based implementations often encounter unstable optimization and limited understanding of code semantics. To address these issues, we introduce ContractGIB, an interpretable graph information bottleneck framework for function-level vulnerability analysis. ContractGIB introduces three main advances. First, ContractGIB introduces an Hilbert–Schmidt Independence Criterion (HSIC) based estimator that provides stable dependence measurement. Second, it incorporates a CodeBERT semantic module to improve node representations. Third, it initializes all nodes with pretrained CodeBERT embeddings, removing the need for hand-crafted features. For each contract function, ContractGIB identifies the most informative nodes forming an instance-specific explanatory subgraph that supports the model’s prediction. Comprehensive experiments on public smart contract datasets, including ESC and VSC, demonstrate that ContractGIB achieves superior performance compared to competitive GNN baselines, while offering clearer, instance-level interpretability.

Keywords

Smart contract vulnerability detection; graph neural networks; information bottleneck; Hilbert-Schmidt Independence Criterion (HSIC)

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