Qiyang Song (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences), Heqing Huang (Institute of Information Engineering, Chinese Academy of Sciences), Xiaoqi Jia (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences), Yuanbo Xie (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences), Jiahao Cao (Institute for Network Sciences and Cyberspace, Tsinghua University)

Reentrancy vulnerabilities in Ethereum smart contracts have caused significant financial losses, prompting the creation of several automated reentrancy detectors. However, these detectors frequently yield a high rate of false positives due to coarse detection rules, often misclassifying contracts protected by anti-reentrancy patterns as vulnerable. Thus, there is a critical need for the development of specialized automated tools to assist these detectors in accurately identifying anti-reentrancy patterns. While existing code analysis techniques show promise for this specific task, they still face significant challenges in recognizing anti-reentrancy patterns. These challenges are primarily due to the complex and varied features of anti-reentrancy patterns, compounded by insufficient prior knowledge about these features.

This paper introduces AutoAR, an automated recognition system designed to explore and identify prevalent anti-reentrancy patterns in Ethereum contracts. AutoAR utilizes a specialized graph representation, RentPDG, combined with a data filtration approach, to effectively capture anti-reentrancy-related semantics from a large pool of contracts. Based on RentPDGs extracted from these contracts, AutoAR employs a recognition model that integrates a graph auto-encoder with a clustering technique, specifically tailored for precise anti-reentrancy pattern identification. Experimental results show AutoAR can assist existing detectors in identifying 12 prevalent anti-reentrancy patterns with 89% accuracy, and when integrated into the detection workflow, it significantly reduces false positives by over 85%.

View More Papers

Dissecting Payload-based Transaction Phishing on Ethereum

Zhuo Chen (Zhejiang University), Yufeng Hu (Zhejiang University), Bowen He (Zhejiang University), Dong Luo (Zhejiang University), Lei Wu (Zhejiang University), Yajin Zhou (Zhejiang University)

Read More

Reinforcement Unlearning

Dayong Ye (University of Technology Sydney), Tianqing Zhu (City University of Macau), Congcong Zhu (City University of Macau), Derui Wang (CSIRO’s Data61), Kun Gao (University of Technology Sydney), Zewei Shi (CSIRO’s Data61), Sheng Shen (Torrens University Australia), Wanlei Zhou (City University of Macau), Minhui Xue (CSIRO's Data61)

Read More

QMSan: Efficiently Detecting Uninitialized Memory Errors During Fuzzing

Matteo Marini (Sapienza University of Rome), Daniele Cono D'Elia (Sapienza University of Rome), Mathias Payer (EPFL), Leonardo Querzoni (Sapienza University of Rome)

Read More

ReThink: Reveal the Threat of Electromagnetic Interference on Power...

Fengchen Yang (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Zihao Dan (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Kaikai Pan (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Chen Yan (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Xiaoyu Ji (Zhejiang University; ZJU QI-ANXIN IoT Security Joint Labratory), Wenyuan Xu (Zhejiang University; ZJU…

Read More