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...

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Main Authors: WU, Yin, XIE, Xiaofei, PENG, Chenyang, LIU, Dijun, WU, Hao, FAN, Ming, LIU, Tin, WANG, Haijun
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9798
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spelling sg-smu-ink.sis_research-107982024-12-12T09:00:03Z AdvSCanner : Generating adversarial smart contracts to exploit reentrancy vulnerabilities using LLM and static analysis WU, Yin XIE, Xiaofei PENG, Chenyang LIU, Dijun WU, Hao FAN, Ming LIU, Tin WANG, Haijun 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. In this paper, we tackle the challenging task of generating ASCs for identified reentrancy vulnerabilities. To address this difficulty, we introduce AdvSCanner, a novel method that leverages the Large Language Model (LLM) and static analysis to automatically generate adversarial smart contracts (ASCs) designed to exploit reentrancy vulnerabilities in victim contracts. The basic idea of AdvSCanner is to extract attack flows associated with reentrancy vulnerabilities using static analysis and utilize them to guide LLM in generating ASCs. To mitigate the inherent inaccuracies in LLM outputs, AdvSCanner incorporates a self-reflection component, which collects compilation and attack-triggering feedback from the generated ASCs and refines the ASC generation if necessary. Experimental evaluations demonstrate the effectiveness of AdvSCanner, achieving a significantly higher success rate (76.41%) compared to baseline methods, which only achieve 6.92% and 18.97%, respectively. Furthermore, a case study illustrates that AdvSCanner can greatly reduce auditing time from 24 hours (without assistance) to approximately 3 hours when used during the auditing process. 2024-10-27T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/9798 info:doi/10.1145/3691620.3695482 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Reentrancy vulnerabilities detection Adversarial smart contracts Large language models LLMS Artificial Intelligence and Robotics Information Security
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Reentrancy vulnerabilities detection
Adversarial smart contracts
Large language models
LLMS
Artificial Intelligence and Robotics
Information Security
spellingShingle Reentrancy vulnerabilities detection
Adversarial smart contracts
Large language models
LLMS
Artificial Intelligence and Robotics
Information Security
WU, Yin
XIE, Xiaofei
PENG, Chenyang
LIU, Dijun
WU, Hao
FAN, Ming
LIU, Tin
WANG, Haijun
AdvSCanner : Generating adversarial smart contracts to exploit reentrancy vulnerabilities using LLM and static analysis
description 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. In this paper, we tackle the challenging task of generating ASCs for identified reentrancy vulnerabilities. To address this difficulty, we introduce AdvSCanner, a novel method that leverages the Large Language Model (LLM) and static analysis to automatically generate adversarial smart contracts (ASCs) designed to exploit reentrancy vulnerabilities in victim contracts. The basic idea of AdvSCanner is to extract attack flows associated with reentrancy vulnerabilities using static analysis and utilize them to guide LLM in generating ASCs. To mitigate the inherent inaccuracies in LLM outputs, AdvSCanner incorporates a self-reflection component, which collects compilation and attack-triggering feedback from the generated ASCs and refines the ASC generation if necessary. Experimental evaluations demonstrate the effectiveness of AdvSCanner, achieving a significantly higher success rate (76.41%) compared to baseline methods, which only achieve 6.92% and 18.97%, respectively. Furthermore, a case study illustrates that AdvSCanner can greatly reduce auditing time from 24 hours (without assistance) to approximately 3 hours when used during the auditing process.
format text
author WU, Yin
XIE, Xiaofei
PENG, Chenyang
LIU, Dijun
WU, Hao
FAN, Ming
LIU, Tin
WANG, Haijun
author_facet WU, Yin
XIE, Xiaofei
PENG, Chenyang
LIU, Dijun
WU, Hao
FAN, Ming
LIU, Tin
WANG, Haijun
author_sort WU, Yin
title AdvSCanner : Generating adversarial smart contracts to exploit reentrancy vulnerabilities using LLM and static analysis
title_short AdvSCanner : Generating adversarial smart contracts to exploit reentrancy vulnerabilities using LLM and static analysis
title_full AdvSCanner : Generating adversarial smart contracts to exploit reentrancy vulnerabilities using LLM and static analysis
title_fullStr AdvSCanner : Generating adversarial smart contracts to exploit reentrancy vulnerabilities using LLM and static analysis
title_full_unstemmed AdvSCanner : Generating adversarial smart contracts to exploit reentrancy vulnerabilities using LLM and static analysis
title_sort advscanner : generating adversarial smart contracts to exploit reentrancy vulnerabilities using llm and static analysis
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9798
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