# ACFIX: Guiding LLMs with Mined Common RBAC Practices for Context-Aware Repair of Access Control Vulnerabilities in Smart Contracts

arXiv: https://arxiv.org/abs/2403.06838 · HTML: https://mlsanity.com/paper/2403.06838/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2024.

## Built on by (most cited first, 15 of 15)

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- [PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation](https://mlsanity.com/paper/2405.02580/) (2024)
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- [Unlocking LLM Repair Capabilities Through Cross-Language Translation and Multi-Agent Refinement](https://mlsanity.com/paper/2503.22512/) (2025)
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- [Towards Secure Program Partitioning for Smart Contracts with LLM's In-Context Learning](https://mlsanity.com/paper/2502.14215/) (2025)
- [UEChecker: Detecting Unchecked External Call Vulnerabilities in DApps via Graph Analysis](https://mlsanity.com/paper/2508.01343/) (2025)
- [Towards Automated Smart Contract Generation: Evaluation, Benchmarking, and Retrieval-Augmented Repair](https://mlsanity.com/paper/2503.01098/) (2025)
- [Towards Understanding Deep Learning Model in Image Recognition via Coverage Test](https://mlsanity.com/paper/2505.08814/) (2025)
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## Abstract (arXiv)

Smart contracts are susceptible to various security issues, among which access control (AC) vulnerabilities are particularly critical. While existing research has proposed multiple detection tools, the automatic and appropriate repair of AC vulnerabilities in smart contracts remains a challenge. Unlike commonly supported vulnerability types by existing repair tools, such as reentrancy, which are usually fixed by template-based approaches, the main obstacle of AC lies in identifying the appropriate roles or permissions amid a long list of non-AC-related source code to generate proper patch code, a task that demands human-level intelligence. Leveraging recent advancements in large language models (LLMs), we employ the state-of-the-art GPT-4 model and enhance it with a novel approach called ACFIX. The key insight is that we can mine common AC practices for major categories of code functionality and use them to guide LLMs in fixing code with similar functionality. To this end, ACFIX involves both offline and online phases. First, during the offline phase, ACFIX mines a taxonomy of common Role-based Access Control (RBAC) practices from 344,251 on-chain contracts, categorizing 49 role-permission pairs from the top 1,000 pairs mined. Second, during the online phase, ACFIX tracks AC-related elements across the contract and uses this context information along with a Chain-of-Thought pipeline to guide LLMs in identifying the most appropriate role-permission pair for the subject contract and subsequently generating a suitable patch. This patch will then undergo a validity and effectiveness check. To evaluate ACFIX, we built the first benchmark dataset of 118 real-world AC vulnerabilities, and our evaluation revealed that ACFIX successfully repaired 94.92% of them. This represents a significant improvement compared to the baseline GPT-4, which achieved only 52.54%.