Soley: Identification and Automated Detection of Logic Vulnerabilities in Ethereum Smart Contracts Using Large Language Models

• URL: http://arxiv.org/abs/2406.16244v1
• Date: Mon, 24 Jun 2024 00:15:18 GMT
• Title: Soley: Identification and Automated Detection of Logic Vulnerabilities in Ethereum Smart Contracts Using Large Language Models
• Authors: Majd Soud, Waltteri Nuutinen, Grischa Liebel,
• Abstract summary: We empirically investigate logic vulnerabilities in real-world smart contracts extracted from code changes on GitHub. We introduce Soley, an automated method for detecting logic vulnerabilities in smart contracts. We examine mitigation strategies employed by smart contract developers to address these vulnerabilities in real-world scenarios.
• Score: 1.081463830315253
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Modern blockchain, such as Ethereum, supports the deployment and execution of so-called smart contracts, autonomous digital programs with significant value of cryptocurrency. Executing smart contracts requires gas costs paid by users, which define the limits of the contract's execution. Logic vulnerabilities in smart contracts can lead to financial losses, and are often the root cause of high-impact cyberattacks. Our objective is threefold: (i) empirically investigate logic vulnerabilities in real-world smart contracts extracted from code changes on GitHub, (ii) introduce Soley, an automated method for detecting logic vulnerabilities in smart contracts, leveraging Large Language Models (LLMs), and (iii) examine mitigation strategies employed by smart contract developers to address these vulnerabilities in real-world scenarios. We obtained smart contracts and related code changes from GitHub. To address the first and third objectives, we qualitatively investigated available logic vulnerabilities using an open coding method. We identified these vulnerabilities and their mitigation strategies. For the second objective, we extracted various logic vulnerabilities, applied preprocessing techniques, and implemented and trained the proposed Soley model. We evaluated Soley along with the performance of various LLMs and compared the results with the state-of-the-art baseline on the task of logic vulnerability detection. From our analysis, we identified nine novel logic vulnerabilities, extending existing taxonomies with these vulnerabilities. Furthermore, we introduced several mitigation strategies extracted from observed developer modifications in real-world scenarios. Our Soley method outperforms existing methods in automatically identifying logic vulnerabilities. Interestingly, the efficacy of LLMs in this task was evident without requiring extensive feature engineering.

Related papers

• LLM-SmartAudit: Advanced Smart Contract Vulnerability Detection [3.1409266162146467]
  This paper introduces LLM-SmartAudit, a novel framework to detect and analyze vulnerabilities in smart contracts. Using a multi-agent conversational approach, LLM-SmartAudit employs a collaborative system with specialized agents to enhance the audit process. Our framework can detect complex logic vulnerabilities that traditional tools have previously overlooked.
  arXiv  Detail & Related papers  (2024-10-12T06:24:21Z)
• Compromising Embodied Agents with Contextual Backdoor Attacks [69.71630408822767]
  Large language models (LLMs) have transformed the development of embodied intelligence. This paper uncovers a significant backdoor security threat within this process. By poisoning just a few contextual demonstrations, attackers can covertly compromise the contextual environment of a black-box LLM.
  arXiv  Detail & Related papers  (2024-08-06T01:20:12Z)
• Vulnerability Detection in Ethereum Smart Contracts via Machine Learning: A Qualitative Analysis [0.0]
  We analyze the state of the art in machine-learning vulnerability detection for smart contracts. We discuss best practices to enhance the accuracy, scope, and efficiency of vulnerability detection in smart contracts.
  arXiv  Detail & Related papers  (2024-07-26T10:09:44Z)
• Contractual Reinforcement Learning: Pulling Arms with Invisible Hands [68.77645200579181]
  We propose a theoretical framework for aligning economic interests of different stakeholders in the online learning problems through contract design. For the planning problem, we design an efficient dynamic programming algorithm to determine the optimal contracts against the far-sighted agent. For the learning problem, we introduce a generic design of no-regret learning algorithms to untangle the challenges from robust design of contracts to the balance of exploration and exploitation.
  arXiv  Detail & Related papers  (2024-07-01T16:53:00Z)
• LookAhead: Preventing DeFi Attacks via Unveiling Adversarial Contracts [15.071155232677643]
  Decentralized Finance (DeFi) incidents have resulted in financial damages exceeding 3 billion US dollars. Current detection tools face significant challenges in identifying attack activities effectively. We propose a new direction for detecting DeFi attacks that focuses on identifying adversarial contracts.
  arXiv  Detail & Related papers  (2024-01-14T11:39:33Z)
• Performance-lossless Black-box Model Watermarking [69.22653003059031]
  We propose a branch backdoor-based model watermarking protocol to protect model intellectual property. In addition, we analyze the potential threats to the protocol and provide a secure and feasible watermarking instance for language models.
  arXiv  Detail & Related papers  (2023-12-11T16:14:04Z)
• CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models [58.27254444280376]
  Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks. Training data for these models is usually collected from the Internet (e.g., from open-source repositories) and is likely to contain faults and security vulnerabilities. This unsanitized training data can cause the language models to learn these vulnerabilities and propagate them during the code generation procedure.
  arXiv  Detail & Related papers  (2023-02-08T11:54:07Z)
• An Automated Vulnerability Detection Framework for Smart Contracts [18.758795474791427]
  We propose a framework to automatically detect vulnerabilities in smart contracts on the blockchain. More specifically, first, we utilize novel feature vector generation techniques from bytecode of smart contract. Next, the collected vectors are fed into our novel metric learning-based deep neural network(DNN) to get the detection result.
  arXiv  Detail & Related papers  (2023-01-20T23:16:04Z)
• Pre-deployment Analysis of Smart Contracts -- A Survey [0.27195102129095]
  We present a systematic review of the literature on smart contract vulnerabilities and methods. Specifically, we enumerate and classify smart contract vulnerabilities and methods by the properties they address. Several patterns about the strengths of different methods emerge through this classification process.
  arXiv  Detail & Related papers  (2023-01-15T12:36:56Z)
• Logically Consistent Adversarial Attacks for Soft Theorem Provers [110.17147570572939]
  We propose a generative adversarial framework for probing and improving language models' reasoning capabilities. Our framework successfully generates adversarial attacks and identifies global weaknesses. In addition to effective probing, we show that training on the generated samples improves the target model's performance.
  arXiv  Detail & Related papers  (2022-04-29T19:10:12Z)
• ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning [80.85273827468063]
  Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable. We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts. We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
  arXiv  Detail & Related papers  (2021-03-23T15:04:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.