Conference item icon

Conference item

SymGPT: auditing smart contracts via combining symbolic execution with large language models

Abstract:
To govern smart contracts running on Ethereum, multiple Ethereum Request for Comment (ERC) standards have been developed, each defining a set of rules governing contract behavior. Violating these rules can cause serious security issues and financial losses, signifying the importance of verifying ERC compliance. Today’s practices of such verification include manual audits, expert-developed program-analysis tools, and large language models (LLMs), all of which remain ineffective at detecting ERC rule violations. This paper introduces SymGPT, a tool that combines LLMs with symbolic execution to automatically verify smart contracts’ compliance with ERC rules. We begin by empirically analyzing 132 ERC rules from three major ERC standards, examining their content, security implications, and natural language descriptions. Based on this study, SymGPT instructs an LLM to translate ERC rules into a domain-specific language, synthesizes constraints from the translated rules to model potential rule violations, and performs symbolic execution for violation detection. Our evaluation shows that SymGPT identifies 5,783 ERC rule violations in 4,000 real- world contracts, including 1,375 violations with clear attack paths for financial theft. Furthermore, SymGPT outperforms six automated techniques and a security-expert auditing service, underscoring its superiority over current smart contract analysis methods.
Publication status:
Accepted
Peer review status:
Peer reviewed

Actions

Access Document

Files:

Authors


More from this funder
Funder identifier:
https://ror.org/001aqnf71
Grant:
10066667
More from this funder
Funder identifier:
https://ror.org/0439y7842
Grant:
EP/T006544/2
EP/T014709/2


Publisher:
Association for Computing Machinery
Journal:
Proceedings of the ACM on Programming Languages More from this journal
Volume:
10
Article number:
109
Acceptance date:
2025-12-17
Event title:
OOPSLA 2026
Event location:
Oakland, California, United States
Event website:
https://2026.splashcon.org/track/oopsla-2026
Event start date:
2026-10-03
Event end date:
2026-10-09
EISSN:
2475-1421


Language:
English
Pubs id:
2383148
Local pid:
pubs:2383148
Deposit date:
2026-03-02
ARK identifier:

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP