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Cited by 41Blockchain Technology Applications and SecuritySoftware Engineering ResearchSoftware Testing and Debugging Techniques

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Smart Contract Vulnerability Detection: The Role of Large Language Model (LLM)

Boi, Biagio, Esposito, Christian, Lee, Sokjoon·2024·ACM SIGAPP Applied Computing Review
DOI 10.1145/3687251.3687253
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Abstract

Smart contracts are susceptible to various vulnerabilities that can lead to significant financial losses. The usage of tools for vulnerabilities is reducing the threats but presents some limitations related to the approach used by the tool itself. This paper presents a novel approach to smart contract vulnerability detection utilizing Large Language Models (LLMs), as a tool to detect all the vulnerabilities at once. Our proposed tool leverages the advanced natural language processing capabilities of LLMs to analyze smart contract code and identify potential security flaws. By training the LLM on a diverse dataset of known smart contract vulnerabilities and secure coding practices, we enhance its ability to recognize subtle and complex vulnerabilities that traditional static analysis tools might miss. The evaluation of our tool demonstrates its effectiveness in detecting a wide range of vulnerabilities with satisfaction and accuracy, providing developers with a robust mechanism to improve the security of their smart contracts before deployment. This approach signifies a significant advancement in the application of artificial intelligence for blockchain security, highlighting the potential of LLMs to enhance the reliability and safety of decentralized applications.

Overview

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Crossref · 10.1145/3687251.3687253

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Citations

Cites

Show all 4 references

Research works printed but not linked: 34

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Cited by

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Metadata

Authors
Boi, B., Esposito, C., & Lee, S.
Affiliations
  • University of Salerno (IT)
  • Gachon University (KR)
ORCID
Venue
ACM SIGAPP Applied Computing Review
Publisher
Association for Computing Machinery (ACM)
Published
2024-06
Volume
24
Issue
2
Pages
19-29
ISSN
  • 1559-6915 (ISSN-L)
  • 1931-0161
DOI
10.1145/3687251.3687253
OpenAlex
W4401361663
Cited by
41
References
4
Citations by year
2026: 16 · 2025: 24 · 2024: 1
FWCI
15.42
Type
journal article
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM SIGAPP Applied Computing Review (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
Keywords
smart contract vulnerability detection, large language models, blockchain security, smart contract security, secure coding, decentralized applications, static analysis limitations

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CoChat. (2026). Citation check: Smart Contract Vulnerability Detection: The Role of Large Language Model (LLM). https://app.cochat.ai/papers/10.1145/3687251.3687253
The paper
Boi, B., Esposito, C., & Lee, S. (2024). Smart Contract Vulnerability Detection: The Role of Large Language Model (LLM). ACM SIGAPP Applied Computing Review. https://doi.org/10.1145/3687251.3687253