Smart Contract and DeFi Security Tools: Do They Meet the Needs of Practitioners?
Not on the map. Why?
Abstract (arXiv)
The growth of the decentralized finance (DeFi) ecosystem built on blockchain technology and smart contracts has led to an increased demand for secure and reliable smart contract development. However, attacks targeting smart contracts are increasing, causing an estimated 6.45 billion in financial losses. Researchers have proposed various automated security tools to detect vulnerabilities, but their real-world impact remains uncertain. In this paper, we aim to shed light on the effectiveness of automated security tools in identifying vulnerabilities that can lead to high-profile attacks, and their overall usage within the industry. Our comprehensive study encompasses an evaluation of five SoTA automated security tools, an analysis of 127 high-impact real-world attacks resulting in 2.3 billion in losses, and a survey of 49 developers and auditors working in leading DeFi protocols. Our findings reveal a stark reality: the tools could have prevented a mere 8% of the attacks in our dataset, amounting to 149 million out of the 2.3 billion in losses. Notably, all preventable attacks were related to reentrancy vulnerabilities. Furthermore, practitioners distinguish logic-related bugs and protocol layer vulnerabilities as significant threats that are not adequately addressed by existing security tools. Our results emphasize the need to develop specialized tools catering to the distinct demands and expectations of developers and auditors. Further, our study highlights the necessity for continuous advancements in security tools to effectively tackle the ever-evolving challenges confronting the DeFi ecosystem.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Apr 2023.
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Citing papers per year: 2024: 1; 2025: 5; 2026: 2.
8 papers.
- 2025SCALM: Detecting Bad Practices in Smart Contracts Through LLMscited by 8
- 2025Adaptive Plan-Execute Framework for Smart Contract Security Auditingcited by 2
- 2024DISL: Fueling Research with A Large Dataset of Solidity Smart Contractscited by 1
- 2025Web3 x AI Agents: Landscape, Integrations, and Foundational Challengescited by 1
- 2025SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contractscited by 0
- 2025On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigationcited by 0
- 2026AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction poolingcited by 0
- 2026Beyond Detection: Agentic Attack Synthesis and Simulation for Smart Contractscited by 0
- 2026Beyond Detection: Agentic Attack Synthesis and Simulation for Smart Contracts17 Jul 2026
- 2026AttackPathGNN: Cross-function vulnerability detection in smart contracts using state interference graphs and conjunction pooling4 Jun 2026
- 2025On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation15 Oct 2025
- 2025SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contracts6 Sep 2025
- 2025Web3 x AI Agents: Landscape, Integrations, and Foundational Challenges4 Aug 2025
- 2025Adaptive Plan-Execute Framework for Smart Contract Security Auditing21 May 2025
- 2025SCALM: Detecting Bad Practices in Smart Contracts Through LLMs4 Feb 2025
- 2024DISL: Fueling Research with A Large Dataset of Solidity Smart Contracts25 Mar 2024