arXiv subjects

Monica Jin

Publications and source records attributed to Monica Jin.

2 recordsLinked to original sources

arXiv2025

On-Chain Analysis of Smart Contract Dependency Risks on Ethereum

In this paper, we present the first large-scale empirical study of smart contract dependencies, analyzing over 41 million contracts and 11 billion interactions on Ethereum up to December 2024. Our results yield four key insights: (1) 59% of contract transactions involve multiple contracts (median of 4 per transaction in 2024) indicating potential smart contract dependency risks; (2) the ecosystem exhibits extreme centralization, with just 11 (0.001%) deployers controlling 20.5 million (50%) of alive contracts, with major risks related to factory contracts and deployer privileges; (3) three most depended-upon contracts are mutable, meaning large parts of the ecosystem rely on contracts that can be altered at any time, which is a significant risk, (4) actual smart contract protocol dependencies are significantly more complex than officially documented, undermining Ethereum's transparency ethos, and creating unnecessary attack surface. Our work provides the first large-scale empirical foundation for understanding smart contract dependency risks, offering crucial insights for developers, users, and security researchers in the blockchain space.

cs.SE↗

arXiv2025

Do Automated Fixes Truly Mitigate Smart Contract Exploits?

Automated Program Repair (APR) for smart contract security promises to automatically mitigate smart contract vulnerabilities responsible for billions in financial losses. However, the true effectiveness of this research in addressing smart contract exploits remains uncharted territory. This paper bridges this critical gap by introducing a novel and systematic experimental framework for evaluating exploit mitigation of program repair tools for smart contracts. We qualitatively and quantitatively analyze 20 state-of-the-art APR tools using a dataset of 143 vulnerable smart contracts, for which we manually craft 91 executable exploits. We are the very first to define and measure the essential "exploit mitigation rate" , giving researchers and practitioners a real sense of effectiveness of cutting edge techniques. Our findings reveal substantial disparities in the state of the art, with an exploit mitigation rate ranging from a low of 29% to a high of 74%. Our study identifies systemic limitations, such as inconsistent functionality preservation, that must be addressed in future research on program repair for smart contracts.

cs.SE↗