Integrated Smart Contract Vulnerability Detection Technology Based on AFL Fuzzing Strategy and a Lightweight Seed Selection Strategy
Keyan Cao, Yuxin Kang, Xinlei Wang, Zhongyang Wang · Applied and Computational Engineering · 2025
With the continuous development of blockchain technology, thousands of smart contracts have been deployed on the blockchain, and the number of smart contract vulnerabilities has increased significantly. In the task of smart contract vulnerability detection, fuzz testing methods are usually used for detection. Existing AFL-based methods are inefficient in generating test cases that meet complex path constraints. This study addresses the limitations of traditional fuzz testing techniques in detecting vulnerabilities related to strictly constrained conditional branches in Ethereum smart contracts. To overcome this challenge, we propose a hybrid framework that combines static semantic analysis with adaptive dynamic fuzz testing and combines a lightweight heuristic seed selection mechanism to prioritize path-sensitive mutations. Our method adopts semantic-aware operators to guide targeted exploration of protected execution paths while dynamically optimizing energy allocation among test cases. Experimental evaluation on benchmark contracts shows that compared with baseline methods, the proposed framework achieves significantly improved branch coverage and accelerated vulnerability detection, especially for critical security vulnerabilities such as reentrancy and arithmetic exceptions, without sacrificing detection accuracy. The results verify the effectiveness of our method in balancing exploration efficiency and analysis rigor for blockchain-oriented security verification.