This paper examines security testing techniques designed to detect reentrancy vulnerabilities in Ethereum smart contracts. Reentrancy can lead to unauthorized state changes and fund siphoning, making it a key target for security testing. In this paper, we evaluate methods such as fuzzing, mutation testing, and symbolic execution, focusing on tools that detect reentrancy attacks and we provide insights into their effectiveness in smart contract security. We also propose a Model-Based Testing (MBT) approach enhanced by Artificial Intelligence (AI) for automated test case generation, improving both coverage and reentrancy vulnerability detection.