Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Background 2.1 Ethereum Primer 2.2 Whitelisted Address Verification 2.3 Taint Analysis on Smart Contracts and 2.4 Threat Model Motivating Example and Challenges 3.1 Motivating Example 3.2 Challenges 3.3 Limitations of Existing Tools Design of AVVERIFIER and 4.1 Overview 4.2 Notations 4.3 Component#1: Code Grapher 4.4 Component#2: EVM Simulator 4.5 Component#3: Vulnerability Detector Evaluation 5.1 Experimental Setup & Research Questions 5.2 RQ1: Effectiveness & Efficiency 5.3 RQ2: Characteristics of Real-world Vulnerable Contracts 5.4 RQ3: Real-time Detection Discussion 6.1 Threats to Validity and 6.2 Limitations 6.3 Ethical Consideration Related Work Conclusion, Availability, and References Background 2.1 Ethereum Primer 2.2 Whitelisted Address Verification 2.3 Taint Analysis on Smart Contracts and 2.4 Threat Model Background 2.1 Ethereum Primer 2.1 Ethereum Primer 2.2 Whitelisted Address Verification 2.2 Whitelisted Address Verification 2.3 Taint Analysis on Smart Contracts and 2.4 Threat Model 2.3 Taint Analysis on Smart Contracts and 2.4 Threat Model Motivating Example and Challenges 3.1 Motivating Example 3.2 Challenges 3.3 Limitations of Existing Tools Motivating Example and Challenges 3.1 Motivating Example 3.1 Motivating Example 3.2 Challenges 3.2 Challenges 3.3 Limitations of Existing Tools 3.3 Limitations of Existing Tools Design of AVVERIFIER and 4.1 Overview 4.2 Notations 4.3 Component#1: Code Grapher 4.4 Component#2: EVM Simulator 4.5 Component#3: Vulnerability Detector Design of AVVERIFIER and 4.1 Overview Design of AVVERIFIER and 4.1 Overview 4.2 Notations 4.2 Notations 4.3 Component#1: Code Grapher 4.3 Component#1: Code Grapher 4.4 Component#2: EVM Simulator 4.4 Component#2: EVM Simulator 4.5 Component#3: Vulnerability Detector 4.5 Component#3: Vulnerability Detector Evaluation 5.1 Experimental Setup & Research Questions 5.2 RQ1: Effectiveness & Efficiency 5.3 RQ2: Characteristics of Real-world Vulnerable Contracts 5.4 RQ3: Real-time Detection Evaluation 5.1 Experimental Setup & Research Questions 5.1 Experimental Setup & Research Questions 5.2 RQ1: Effectiveness & Efficiency 5.2 RQ1: Effectiveness & Efficiency 5.3 RQ2: Characteristics of Real-world Vulnerable Contracts 5.3 RQ2: Characteristics of Real-world Vulnerable Contracts 5.4 RQ3: Real-time Detection 5.4 RQ3: Real-time Detection Discussion 6.1 Threats to Validity and 6.2 Limitations 6.3 Ethical Consideration Discussion 6.1 Threats to Validity and 6.2 Limitations 6.1 Threats to Validity and 6.2 Limitations 6.3 Ethical Consideration 6.3 Ethical Consideration Related Work Related Work Related Work Conclusion, Availability, and References Conclusion, Availability, and References Conclusion, Availability, and References 7 Related Work Smart Contract Vulnerability Detection. Vulnerability detection in smart contracts employs varied methodologies dependent on input types and detection principles. Analyzing contracts can either occur at the high-level source code [35,55] or through the bytecode interfacing with the EVM [4, 38]. Further categorizing based on analytical techniques, static analysis delves into code structure and inherent semantics to identify vulnerabilities, often applied to source code evaluations. Dynamic analyses, more prevalent in bytecode assessments, utilize strategies such as fuzz testing to spot anomalies by bombarding contracts with randomized inputs [10, 44]. In addition to these, hybrid analysis methods are also gaining traction, combining static and dynamic analysis techniques for a more thorough examination of smart contracts. Through this combined approach, hybrid analysis helps in identifying a wider range of vulnerabilities [49, 57]. Furthermore, tracebased evaluations provide a unique perspective by scrutinizing historical transaction patterns, unearthing vulnerabilities from real-world usage patterns [23, 79]. Smart Contract Vulnerability Detection. Taint Analysis in Smart Contracts. Pioneering efforts in taint analysis have led to the development of tools tailored specifically for Ethereum contracts, adept at detecting common vulnerabilities such as unchecked send and reentrancy [6, 66]. Other research efforts have heightened the granularity of taint analysis to uncover intricate data leaks and permission oversights [72, 73]. Furthermore, several advanced methods have integrated both static and dynamic analysis techniques, blending the advantages of both to provide a more comprehensive security assessment [35, 47]. These collective advancements underscore the pivotal role of taint analysis in shaping a robust ecosystem for smart contracts. Taint Analysis in Smart Contracts. Permission Checks and Access Control. Permission checks underpin smart contract security, preventing unauthorized actions which can lead to financial or data losses [22,52]. Many tools and frameworks now aid developers in verifying permissions [45, 67]. However, some advanced attacks can bypass traditional checks, underscoring the need for context-aware analysis [27, 80]. Rigorous permission validation remains crucial for a secure smart contract environment. Permission Checks and Access Control. 8 Conclusion In this work, we present AVVERIFIER, a taint analyzer based on static EVM opcode simulation, which is designed for identifying the address verification vulnerability hidden in Ethereum smart contracts. With the help of the heuristic-based path selection method and taint propagation rules in Simulator, as well as the three-phase formal detection rules implemented in Detector, AVVERIFIER significantly outperforms Mythril in both terms of efficiency and effectiveness. 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Authors: (1) Tianle Sun, Huazhong University of Science and Technology; (2) Ningyu He, Peking University; (3) Jiang Xiao, Huazhong University of Science and Technology; (4) Yinliang Yue, Zhongguancun Laboratory; (5) Xiapu Luo, The Hong Kong Polytechnic University; (6) Haoyu Wang, Huazhong University of Science and Technology. Authors: Authors (1) Tianle Sun, Huazhong University of Science and Technology; (2) Ningyu He, Peking University; (3) Jiang Xiao, Huazhong University of Science and Technology; (4) Yinliang Yue, Zhongguancun Laboratory; (5) Xiapu Luo, The Hong Kong Polytechnic University; (6) Haoyu Wang, Huazhong University of Science and Technology. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv
