AdvSCanner: Generating Adversarial Smart Contracts to Exploit Reentrancy Vulnerabilities Using LLM and Static Analysis
Smart contracts are prone to vulnerabilities, with reentrancy attacks posing significant risks due to their destructive potential. While various methods exist for detecting reentrancy vulnerabilities in smart contracts, such as static analysis, these approaches often suffer from high false positive rates and lack the ability to directly illustrate how vulnerabilities can be exploited in attacks.
Authors
- Dijun Liu
- Xiaofei Xie (ORCID: https://orcid.org/0000-0002-1288-6502)
- Yin Wu (ORCID: https://orcid.org/0000-0003-4229-7562)
- Haijun Wang (ORCID: https://orcid.org/0009-0001-3509-3919)
- Ming Yu Fan (ORCID: https://orcid.org/0000-0002-9327-0987)
- Ting Liu (ORCID: https://orcid.org/0000-0002-7600-0934)
- Chengyu Peng (ORCID: https://orcid.org/0000-0002-8097-8011)
- Hao Wu (ORCID: https://orcid.org/0009-0008-2080-6497)
Institutions
- China Mobile (China) (CN)
- Singapore Management University (SG)
- Xi'an Jiaotong University (CN)
Publication Details
- Published
- 2024-10-18
- DOI
- https://doi.org/10.1145/3691620.3695482
- Citations
- 8
- Primary Topic
- Blockchain Technology Applications and Security
- Type
- article
- Field-Weighted Citation Impact
- 6.23
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China