CKG-LLM: LLM-Assisted Detection of Smart Contract Access Control Vulnerabilities Based on Knowledge Graphs
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917405536550912 |
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| author | Li, Xiaoqi Kuang, Hailu Li, Wenkai Li, Zongwei Ye, Shipeng |
| author_facet | Li, Xiaoqi Kuang, Hailu Li, Wenkai Li, Zongwei Ye, Shipeng |
| contents | Traditional approaches for smart contract analysis often rely on intermediate representations such as abstract syntax trees, control-flow graphs, or static single assignment form. However, these methods face limitations in capturing both semantic structures and control logic. Knowledge graphs, by contrast, offer a structured representation of entities and relations, enabling richer intermediate abstractions of contract code and supporting the use of graph query languages to identify rule-violating elements. This paper presents CKG-LLM, a framework for detecting access-control vulnerabilities in smart contracts. Leveraging the reasoning and code generation capabilities of large language models, CKG-LLM translates natural-language vulnerability patterns into executable queries over contract knowledge graphs to automatically locate vulnerable code elements. Experimental evaluation demonstrates that CKG-LLM achieves superior performance in detecting access-control vulnerabilities compared to existing tools. Finally, we discuss potential extensions of CKG-LLM as part of future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06846 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CKG-LLM: LLM-Assisted Detection of Smart Contract Access Control Vulnerabilities Based on Knowledge Graphs Li, Xiaoqi Kuang, Hailu Li, Wenkai Li, Zongwei Ye, Shipeng Cryptography and Security Traditional approaches for smart contract analysis often rely on intermediate representations such as abstract syntax trees, control-flow graphs, or static single assignment form. However, these methods face limitations in capturing both semantic structures and control logic. Knowledge graphs, by contrast, offer a structured representation of entities and relations, enabling richer intermediate abstractions of contract code and supporting the use of graph query languages to identify rule-violating elements. This paper presents CKG-LLM, a framework for detecting access-control vulnerabilities in smart contracts. Leveraging the reasoning and code generation capabilities of large language models, CKG-LLM translates natural-language vulnerability patterns into executable queries over contract knowledge graphs to automatically locate vulnerable code elements. Experimental evaluation demonstrates that CKG-LLM achieves superior performance in detecting access-control vulnerabilities compared to existing tools. Finally, we discuss potential extensions of CKG-LLM as part of future research directions. |
| title | CKG-LLM: LLM-Assisted Detection of Smart Contract Access Control Vulnerabilities Based on Knowledge Graphs |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2512.06846 |