An Analysis of Static Detection Approaches for Ethereum Smart Contract Vulnerabilities

Abstract

Smart contracts are a core component of blockchain-based systems, enabling decentralized applications to autonomously manage assets and enforce program logic. However, vulnerabilities in smart contracts can cause severe financial losses because of their immutability and public accessibility. As a result, analyzing common attack vectors and evaluating static detection techniques before deployment remain critical challenges in blockchain security. This thesis presents an analytical study of high-impact smart contract attack classes and evaluates the effectiveness of AST-based static detection approaches for Ethereum smart contracts. Building on the Aderyn static analysis framework, custom detectors are implemented to analyze structural code patterns that enable reentrancy attacks, authorization bypass vulnerabilities, and unsafe proxy delegation and storage collision risks. Rather than proposing new defensive mechanisms, this work systematically analyzes how known attacks arise from insecure smart contract programming practices and examines how defensive coding patterns can be identified at the source-code level through static analysis. The effectiveness of the analysis is evaluated using vulnerable smart contract implementations and corresponding exploit scenarios developed with the Foundry testing framework. Experimental results show that the implemented detectors successfully identify exploitable vulnerabilities and demonstrate a strong correspondence between attack-enabling code structures and statically detectable patterns, confirming alignment between static analysis findings and real-world attack behavior. This work demonstrates that extensible AST-based static analysis provides a practical foundation for analyzing both smart contract attacks and the defensive patterns intended to mitigate them before deployment.