SELLM: An Integrated Tool Leveraging Symbolic Execution and LLMS for Smart Contract Vulnerability Detection

Abstract

The immutability of blockchain systems makes the security of smart contracts particularly critical. This study presents SELLM, a novel smart contract vulnerability detection tool that integrates symbolic execution with large language models. SELLM leverages symbolic execution to identify vulnerability-prone paths, extracting control flow data, call sequences, and variable constraints, which are then structured into optimized prompts for LLM analysis. Experimental results using the SmartBugs dataset demonstrate that SELLM substantially outperforms baseline methods in terms of precision and recall, achieving a recall of 90.5% and precision of 89.4% with GPT-4o, particularly excelling in detecting vulnerabilities like arithmetic overflow, bad randomness, and reentrancy.