Demystifying and Detecting Cryptographic Defects in Ethereum Smart ContractsBlockchainAward Winner
To enhance smart contracts with cryptographic capabilities, Ethereum has officially provided a set of system-level cryptographic APIs, such as ecrecover. These APIs have been utilized in over 10% of Ethereum transactions, motivating developers to implement various on-chain cryptographic tasks, such as digital signatures. However, since developers may not always be cryptographic experts, their ad-hoc and potentially defective implementations could compromise the theoretical guarantees of cryptography, leading to real-world security issues. To mitigate this threat, we conducted the first study aimed at demystifying and detecting cryptographic defects in smart contracts. Through the analysis of 2,406 real-world security reports, we defined nine types of cryptographic defects in smart contracts with detailed descriptions and practical detection patterns. Based on this categorization, we proposed CrySol, a fuzzing-based tool to automate the detection of cryptographic defects in smart contracts. It combines transaction replaying and dynamic taint analysis to extract fine-grained crypto-related semantics and employs crypto-specific strategies to guide the test case generation process. urthermore, we collected a large-scale dataset containing 25,745 real-world crypto-related smart contracts and evaluated CrySol’s effectiveness on it. The result demonstrated that CrySol achieves an overall precision of 95.4% and a recall of 91.2%. Notably, CrySol revealed that 5,847 (22.7%) out of 25,745 contracts contain at least one cryptographic defect, highlighting the prevalence of these defects.
Fri 2 MayDisplayed time zone: Eastern Time (US & Canada) change
16:00 - 17:30 | |||
16:00 15mTalk | An Empirical Study of Proxy Smart Contracts at the Ethereum Ecosystem ScaleBlockchain Research Track Mengya Zhang The Ohio State University, Preksha Shukla George Mason University, Wuqi Zhang Mega Labs, Zhuo Zhang Purdue University, Pranav Agrawal George Mason University, Zhiqiang Lin The Ohio State University, Xiangyu Zhang Purdue University, Xiaokuan Zhang George Mason University | ||
16:15 15mTalk | Demystifying and Detecting Cryptographic Defects in Ethereum Smart ContractsBlockchainAward Winner Research Track Jiashuo Zhang Peking University, China, Yiming Shen Sun Yat-sen University, Jiachi Chen Sun Yat-sen University, Jianzhong Su Sun Yat-sen University, Yanlin Wang Sun Yat-sen University, Ting Chen University of Electronic Science and Technology of China, Jianbo Gao Peking University, Zhong Chen | ||
16:30 15mTalk | Chord: Towards a Unified Detection of Blockchain Transaction Parallelism BugsBlockchain Research Track Yuanhang Zhou Tsinghua University, Zhen Yan Tsinghua University, Yuanliang Chen Tsinghua University, Fuchen Ma Tsinghua University, Ting Chen University of Electronic Science and Technology of China, Yu Jiang Tsinghua University | ||
16:45 15mTalk | Definition and Detection of Centralization Defects in Smart ContractsBlockchain Research Track Zewei Lin Sun Yat-sen University, Jiachi Chen Sun Yat-sen University, Jiajing Wu Sun Yat-sen University, Weizhe Zhang Harbin Institute of Technology, Zibin Zheng Sun Yat-sen University | ||
17:00 15mTalk | Fork State-Aware Differential Fuzzing for Blockchain Consensus ImplementationsBlockchain Research Track Won Hoi Kim KAIST, Hocheol Nam KAIST, Muoi Tran ETH Zurich, Amin Jalilov KAIST, Zhenkai Liang National University of Singapore, Sang Kil Cha KAIST, Min Suk Kang KAIST DOI Pre-print | ||
17:15 15mTalk | Code Cloning in Solidity Smart Contracts: Prevalence, Evolution, and Impact on DevelopmentBlockchain Research Track Ran Mo Central China Normal University, Haopeng Song Central China Normal University, Wei Ding Central China Normal University, Chaochao Wu Central China Normal University |