Smart contracts are prone to vulnerabilities, with reentrancy attacks posing significant risks due to their destructive potential. While various methods exist for detecting reentrancy vulnerabilities in smart contracts, such as static analysis, these approaches often suffer from high false positive rates and lack the ability to directly illustrate how detected vulnerabilities can be exploited in attacks.

In this paper, we tackle the challenging task of generating exploits for identified reentrancy vulnerabilities. To address this difficulty, we introduce AdvSCanner, a novel method that leverages Large Language Models (LLMs) and static analysis to automatically generate adversarial smart contracts (ASCs) designed to exploit reentrancy vulnerabilities in victim contracts. The basic idea of AdvSCanner is to extract attack flows associated with reentrancy vulnerabilities using static analysis and utilize them to guide LLMs in generating ASCs. To mitigate the inherent inaccuracies in LLM outputs, AdvSCanner incorporates a self-reflection component, which collects compilation and attack triggering feedback from the generated ASCs and refines the ASC generation if necessary. Experimental evaluations demonstrate the effectiveness of AdvSCanner, achieving a significantly higher success rate (85.90%) compared to the best baseline method, which only achieves 32.05%. Furthermore, a case study illustrates that AdvSCanner can greatly reduce auditing time from 24 hours (without assistance) to approximately 3 hours when used during the auditing process.