Once deployed, blockchain smart contracts are characterized by difficulty in rollback, direct asset binding, and publicly reproducible attack chains. Therefore, vulnerability detection must be conducted as early as possible, before deployment. Compared to source code-centric methods, static detection methods targeting bytecode are more suitable for unpublished contracts, contracts compiled and deployed to the blockchain, and cross-platform auditing. This paper, based on research on smart contract vulnerability detection over the past three years, studies multi-view static vulnerability detection at the bytecode level, focusing on low false positives, low false negatives, and low latency. The model uses opcode sequences, control flow graphs, and dangerous semantic triggering patterns as primary inputs, employing lightweight graph representation, sequence context encoding, and multi-branch belief fusion to identify reentrancy, timestamp dependencies, access control, and self-destruct vulnerabilities. The research methodology follows a "current situation analysis - problem summarization - model design - literature evidence comparison" approach, primarily addressing issues such as unobservable bytecode, cross-version EVM semantic drift, high modeling costs for long sequences, and poor performance due to sparse labels. Based on experimental results from publicly available English literature over the past three years, this paper summarizes the representative model data scale, reporting accuracy, and applicability. The results show that bytecode static detection has evolved from rule-based detection to graph neural networks, Transformers, and multimodal fusion, but significant shortcomings remain in terms of unified benchmarks, interpretability, and online deployment latency. This paper proposes a model framework that can provide methodological support for blockchain auditing platforms, pre-transaction risk control, and contract market access review.

Bingyan Hu. Research on Static Vulnerability Detection Model at the Bytecode Level of Blockchain Smart Contracts. International Journal of Neural Network (2026), Vol. 5, Issue 1: 42-51. https://doi.org/10.38007/NN.2026.050105.

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