# CKG-LLM: LLM-Assisted Detection of Smart Contract Access Control Vulnerabilities Based on Knowledge Graphs

arXiv: https://arxiv.org/abs/2512.06846 · HTML: https://mlsanity.com/paper/2512.06846/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2025.

## Built on by (most cited first, 2 of 2)

- [Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization](https://mlsanity.com/paper/2603.26270/) (2026)
- [RAGas: Retrieval-Augmented Gas Optimization for Smart Contracts with Continuous Knowledge Integration](https://mlsanity.com/paper/2608.15857/) (2026)

## Abstract (arXiv)

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.