We present LISA, an agentic smart contract vulnerability detection framework that combines rule-based and logic-based methods to address a broad spectrum of vulnerabilities in smart contracts. LISA leverages data from historical audit reports to learn the detection experience (without model fine-tuning), enabling it to generalize learned patterns to unseen projects and evolving threat profiles. In our evaluation, LISA significantly outperforms both LLM-based approaches and traditional static analysis tools, achieving superior coverage of vulnerability types and higher detection accuracy. Our results suggest that LISA offers a compelling solution for industry: delivering more reliable and comprehensive vulnerability detection while reducing the dependence on manual effort.

翻译：本文提出LISA——一种结合基于规则与基于逻辑方法的智能体化智能合约漏洞检测框架，旨在应对智能合约中的广泛漏洞类型。LISA利用历史审计报告数据学习检测经验（无需模型微调），使其能够将习得的模式泛化至未见项目及持续演变的威胁场景。评估结果表明，LISA在漏洞类型覆盖率和检测准确率方面均显著优于基于大语言模型的方法与传统静态分析工具，展现出卓越性能。我们的研究证实LISA为产业界提供了具有吸引力的解决方案：在降低对人工依赖的同时，提供更可靠、更全面的漏洞检测能力。