ETrace:Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915417428066304 |
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| author | Peng, Chenyang Wang, Haijun Wu, Yin Wu, Hao Fan, Ming Zhao, Yitao Liu, Ting |
| author_facet | Peng, Chenyang Wang, Haijun Wu, Yin Wu, Hao Fan, Ming Zhao, Yitao Liu, Ting |
| contents | With the advance application of blockchain technology in various fields, ensuring the security and stability of smart contracts has emerged as a critical challenge. Current security analysis methodologies in vulnerability detection can be categorized into static analysis and dynamic analysis methods.However, these existing traditional vulnerability detection methods predominantly rely on analyzing original contract code, not all smart contracts provide accessible code.We present ETrace, a novel event-driven vulnerability detection framework for smart contracts, which uniquely identifies potential vulnerabilities through LLM-powered trace analysis without requiring source code access. By extracting fine-grained event sequences from transaction logs, the framework leverages Large Language Models (LLMs) as adaptive semantic interpreters to reconstruct event analysis through chain-of-thought reasoning. ETrace implements pattern-matching to establish causal links between transaction behavior patterns and known attack behaviors. Furthermore, we validate the effectiveness of ETrace through preliminary experimental results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_15790 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ETrace:Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis Peng, Chenyang Wang, Haijun Wu, Yin Wu, Hao Fan, Ming Zhao, Yitao Liu, Ting Cryptography and Security Software Engineering D.2.5 With the advance application of blockchain technology in various fields, ensuring the security and stability of smart contracts has emerged as a critical challenge. Current security analysis methodologies in vulnerability detection can be categorized into static analysis and dynamic analysis methods.However, these existing traditional vulnerability detection methods predominantly rely on analyzing original contract code, not all smart contracts provide accessible code.We present ETrace, a novel event-driven vulnerability detection framework for smart contracts, which uniquely identifies potential vulnerabilities through LLM-powered trace analysis without requiring source code access. By extracting fine-grained event sequences from transaction logs, the framework leverages Large Language Models (LLMs) as adaptive semantic interpreters to reconstruct event analysis through chain-of-thought reasoning. ETrace implements pattern-matching to establish causal links between transaction behavior patterns and known attack behaviors. Furthermore, we validate the effectiveness of ETrace through preliminary experimental results. |
| title | ETrace:Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis |
| topic | Cryptography and Security Software Engineering D.2.5 |
| url | https://arxiv.org/abs/2506.15790 |