Ai-Driven Vulnerability Analysis in Smart Contracts: Trends, Challenges and Future Directions
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908398630469632 |
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| author | Ozdag, Mesut |
| author_facet | Ozdag, Mesut |
| contents | Smart contracts, integral to blockchain ecosystems, enable decentralized applications to execute predefined operations without intermediaries. Their ability to enforce trustless interactions has made them a core component of platforms such as Ethereum. Vulnerabilities such as numerical overflows, reentrancy attacks, and improper access permissions have led to the loss of millions of dollars throughout the blockchain and smart contract sector. Traditional smart contract auditing techniques such as manual code reviews and formal verification face limitations in scalability, automation, and adaptability to evolving development patterns. As a result, AI-based solutions have emerged as a promising alternative, offering the ability to learn complex patterns, detect subtle flaws, and provide scalable security assurances. This paper examines novel AI-driven techniques for vulnerability detection in smart contracts, focusing on machine learning, deep learning, graph neural networks, and transformer-based models. This paper analyzes how each technique represents code, processes semantic information, and responds to real world vulnerability classes. We also compare their strengths and weaknesses in terms of accuracy, interpretability, computational overhead, and real time applicability. Lastly, it highlights open challenges and future opportunities for advancing this domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06735 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ai-Driven Vulnerability Analysis in Smart Contracts: Trends, Challenges and Future Directions Ozdag, Mesut Cryptography and Security Artificial Intelligence Smart contracts, integral to blockchain ecosystems, enable decentralized applications to execute predefined operations without intermediaries. Their ability to enforce trustless interactions has made them a core component of platforms such as Ethereum. Vulnerabilities such as numerical overflows, reentrancy attacks, and improper access permissions have led to the loss of millions of dollars throughout the blockchain and smart contract sector. Traditional smart contract auditing techniques such as manual code reviews and formal verification face limitations in scalability, automation, and adaptability to evolving development patterns. As a result, AI-based solutions have emerged as a promising alternative, offering the ability to learn complex patterns, detect subtle flaws, and provide scalable security assurances. This paper examines novel AI-driven techniques for vulnerability detection in smart contracts, focusing on machine learning, deep learning, graph neural networks, and transformer-based models. This paper analyzes how each technique represents code, processes semantic information, and responds to real world vulnerability classes. We also compare their strengths and weaknesses in terms of accuracy, interpretability, computational overhead, and real time applicability. Lastly, it highlights open challenges and future opportunities for advancing this domain. |
| title | Ai-Driven Vulnerability Analysis in Smart Contracts: Trends, Challenges and Future Directions |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2506.06735 |