一句话：构建180个真实访问控制漏洞基准，系统评测SOTA工具与LLM，揭示检测盲区并倡导混合方法。
访问控制漏洞是智能合约最关键的安全威胁之一，虽经大量研究仍在以太坊生态中广泛存在且危害严重。本文构建包含180个真实访问控制漏洞的基准数据集，数据来自CVE、DeFiHackLabs事件与Code4rena审计报告，并从三个维度开展系统性基准研究：提出基于成因的分类体系，分析漏洞的普遍性与演化；评测六款SOTA检测工具（两款工业界、四款学术界），发现召回率仅为3%–8%且存在显著盲区；检查120万个已部署合约，揭示现有工具忽视的实际防护缺口。文章还评估大语言模型（LLM）的检测潜力，显示其可发现53%–75%的漏洞，优于传统工具，但面临幻觉与可扩展性挑战。结果强调需要结合静态分析与LLM语义推理的混合方法。
Access control (AC) vulnerabilities are among the most critical security threats to smart contracts. Despite extensive research, they remain widespread and damaging in the Ethereum ecosystem. To understand and advance the current state-of-the-art (SOTA) in AC vulnerability detection, we first curate a diverse dataset of 180 real-world AC vulnerabilities from CVE entries, DeFiHackLabs incidents, and Code4rena audit reports.Using this dataset, we conduct a systematic benchmark study along three dimensions. First, we develop a cause-based taxonomy and analyze the prevalence and evolution of AC vulnerabilities. Second, we evaluate six SOTA tools, including two from industry and four from academia, revealing low recall (3% to 8%) and significant blind spots. To understand these failures, we examine 1.2 million deployed contracts and uncover practical gaps in AC protection mechanisms overlooked by existing tools. Finally, we assess the potential of large language models (LLMs) for AC vulnerability detection and show that LLMs detect 53–75% of vulnerabilities, outperforming traditional tools but facing challenges such as hallucinations and scalability. Our findings highlight the need for hybrid approaches that combine static analysis with LLM-based semantic reasoning to address the complexity of modern AC vulnerabilities.
DOI 原文 ·
@inproceedings{paperbot9460,
title = {Have We Solved Access Control Vulnerability Detection in Smart Contracts? A Benchmark Study},
author = {Han Liu and Daoyuan Wu and Yuqiang Sun and Shuai Wang and Yang Liu},
booktitle = {ASE},
year = {2025},
doi = {10.1109/ase63991.2025.00166}
}