🤖 AI Summary
Frequent and increasingly severe smart contract vulnerabilities necessitate efficient dynamic analysis techniques for real-time intrusion detection and advanced forensic investigation.
Method: This paper proposes a dynamic analysis framework tailored for the Ethereum Virtual Machine (EVM), introducing the Execution Property Graph (EPG)—a novel unified representation of runtime contract behavior—by integrating dynamic instrumentation, EVM execution tracing, and property graph modeling. We further design a lightweight, customized graph traversal algorithm to enable high-accuracy, low-latency attack pattern recognition.
Contribution/Results: Experimental evaluation demonstrates rapid per-transaction graph traversal, high true positive rate, and successful discovery of a zero-day vulnerability affecting Uniswap. Our framework establishes a scalable, general-purpose dynamic analysis paradigm for smart contract security, bridging critical gaps in runtime behavior modeling and actionable threat detection.
📝 Abstract
Identifying and mitigating vulnerabilities in smart contracts is crucial, especially considering the rapid growth and increasing complexity of Decentralized Finance (DeFi) platforms. To address the challenges associated with securing these contracts, we introduce a versatile dynamic analysis framework specifically designed for the Ethereum Virtual Machine (EVM). This comprehensive framework focuses on tracking contract executions, capturing valuable runtime information, while introducing and employing the Execution Property Graph (EPG) to propose a unique graph traversal technique that swiftly detects potential smart contract attacks. Our approach showcases its efficacy with rapid average graph traversal time per transaction and high true positive rates. The successful identification of a zero-day vulnerability affecting Uniswap highlights the framework's potential to effectively uncover smart contract vulnerabilities in complex DeFi systems.
