69 citations · 92 across the 35 of their papers we have counts for
12 papers · 1 filter
ScamSweeper: Detecting Illegal Accounts in Web3 Scams via Transactions Analysis
Xiaoqi Li, Wenkai Li, Zhijie Liu +6
The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by…
CKG-LLM: LLM-Assisted Detection of Smart Contract Access Control Vulnerabilities Based on Knowledge Graphs
Xiaoqi Li, Hailu Kuang, Wenkai Li +2
Traditional approaches for smart contract analysis often rely on intermediate representations such as abstract syntax trees, control-flow graphs, or static single assignment form.…
Beyond the Hype: A Large-Scale Empirical Analysis of On-Chain Transactions in NFT Scams
Wenkai Li, Zongwei Li, Xiaoqi Li +3
Non-fungible tokens (NFTs) serve as a representative form of digital asset ownership and have attracted numerous investors, creators, and tech enthusiasts in recent years. However,…
Penetrating the Hostile: Detecting DeFi Protocol Exploits through Cross-Contract Analysis
Xiaoqi Li, Wenkai Li, Zhiquan Liu +2
Decentralized finance (DeFi) protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exce…
NATLM: Detecting Defects in NFT Smart Contracts Leveraging LLM
Yuanzheng Niu, Xiaoqi Li, Wenkai Li
Security issues are becoming increasingly significant with the rapid evolution of Non-fungible Tokens (NFTs). As NFTs are traded as digital assets, they have emerged as prime targe…
MultiCFV: Detecting Control Flow Vulnerabilities in Smart Contracts Leveraging Multimodal Deep Learning
Hongli Peng, Xiaoqi Li, Wenkai Li
The introduction of smart contract functionality marks the advent of the blockchain 2.0 era, enabling blockchain technology to support digital currency transactions and complex dis…
UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysis
Dechao Kong, Xiaoqi Li, Wenkai Li
The increasing number of attacks on the contract layer of DApps has resulted in economic losses amounting to $66 billion. Vulnerabilities arise when contracts interact with externa…
Towards Understanding Deep Learning Model in Image Recognition via Coverage Test
Wenkai Li, Xiaoqi Li, Yingjie Mao +1
Deep neural networks (DNNs) play a crucial role in the field of artificial intelligence, and their security-related testing has been a prominent research focus. By inputting test c…
Mining Characteristics of Vulnerable Smart Contracts Across Lifecycle Stages
Hongli Peng, Xiaoqi Li, Wenkai Li
Smart contracts are the cornerstone of decentralized applications and financial protocols, which extend the application of digital currency transactions. The applications and finan…
Enhancing Smart Contract Vulnerability Detection in DApps Leveraging Fine-Tuned LLM
Jiuyang Bu, Wenkai Li, Zongwei Li +2
Decentralized applications (DApps) face significant security risks due to vulnerabilities in smart contracts, with traditional detection methods struggling to address emerging and…
SmartBugBert: BERT-Enhanced Vulnerability Detection for Smart Contract Bytecode
Jiuyang Bu, Wenkai Li, Zongwei Li +2
Smart contracts deployed on blockchain platforms are vulnerable to various security vulnerabilities. However, only a small number of Ethereum contracts have released their source c…
SCALM: Detecting Bad Practices in Smart Contracts Through LLMs
Zongwei Li, Xiaoqi Li, Wenkai Li +1
As the Ethereum platform continues to mature and gain widespread usage, it is crucial to maintain high standards of smart contract writing practices. While bad practices in smart c…