Leveraging Fine-Tuned Language Models for Efficient and Accurate Smart Contract Auditing

• URL: http://arxiv.org/abs/2410.13918v1
• Date: Thu, 17 Oct 2024 09:09:09 GMT
• Title: Leveraging Fine-Tuned Language Models for Efficient and Accurate Smart Contract Auditing
• Authors: Zhiyuan Wei, Jing Sun, Zijian Zhang, Xianhao Zhang, Meng Li,
• Abstract summary: This paper investigates the feasibility of using smaller, fine-tuned models to achieve comparable or even superior results in smart contract auditing. We introduce the FTSmartAudit framework, which is designed to develop cost-effective, specialized models for smart contract auditing. Our contributions include: (1) a single-task learning framework that streamlines data preparation, training, evaluation, and continuous learning; (2) a robust dataset generation method utilizing domain-special knowledge distillation to produce high-quality datasets from advanced models like GPT-4o; and (3) an adaptive learning strategy to maintain model accuracy and robustness.
• Score: 5.65127016235615
• License:
• Abstract: The rise of blockchain technologies has greatly accelerated the development and deployment of smart contracts. However, their inherent vulnerabilities and susceptibility to bugs have led to significant financial losses, underscoring the challenges in securing smart contracts. While traditional auditing methods are crucial, they often fall short in addressing the increasing complexity and volume of smart contracts. Recent advancements in Large Language Models (LLMs) offer promising solutions for enhancing software auditing by automatically identifying security vulnerabilities. Despite their potential, the practical application of these models is hindered by substantial computational demands. This paper investigates the feasibility of using smaller, fine-tuned models to achieve comparable or even superior results in smart contract auditing. We introduce the FTSmartAudit framework, which is designed to develop cost-effective, specialized models for smart contract auditing through the fine-tuning of LLMs. Our contributions include: (1) a single-task learning framework that streamlines data preparation, training, evaluation, and continuous learning; (2) a robust dataset generation method utilizing domain-special knowledge distillation to produce high-quality datasets from advanced models like GPT-4o; (3) an adaptive learning strategy to maintain model accuracy and robustness; (4) the proven effectiveness of fine-tuned models in detecting specific vulnerabilities and complex logical errors; and (5) a framework that can be extended to other domains requiring LLM solutions. Our experimental results demonstrate that smaller models can surpass state-of-the-art commercial models and tools in detecting vulnerabilities in smart contracts.

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)
• SIaM: Self-Improving Code-Assisted Mathematical Reasoning of Large Language Models [54.78329741186446]
  We propose a novel paradigm that uses a code-based critic model to guide steps including question-code data construction, quality control, and complementary evaluation. Experiments across both in-domain and out-of-domain benchmarks in English and Chinese demonstrate the effectiveness of the proposed paradigm.
  arXiv  Detail & Related papers  (2024-08-28T06:33:03Z)
• 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)
• Vulnerability Detection in Smart Contracts: A Comprehensive Survey [10.076412566428756]
  This study examines the potential of machine learning techniques to improve the detection and mitigation of vulnerabilities in smart contracts. We analysed 88 articles published between 2018 and 2023 from the following databases: IEEE, ACM, ScienceDirect, Scopus, and Google Scholar. The findings reveal that classical machine learning techniques, including KNN, RF, DT, XG-Boost, and SVM, outperform static tools in vulnerability detection.
  arXiv  Detail & Related papers  (2024-07-08T11:51:15Z)
• Soley: Identification and Automated Detection of Logic Vulnerabilities in Ethereum Smart Contracts Using Large Language Models [1.081463830315253]
  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.
  arXiv  Detail & Related papers  (2024-06-24T00:15:18Z)
• An Empirical Study of AI-based Smart Contract Creation [4.801455786801489]
  Large language models (LLMs) like ChatGPT and Google Palm2 for smart contract generation seem to be the first well-established instance of an AI pair programmer. The main objective of this study is to assess the quality of generated code provided by LLMs for smart contracts.
  arXiv  Detail & Related papers  (2023-08-05T21:38:57Z)
• Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks [90.11273439036455]
  Large Language Models (LLMs) have shown promising performance in knowledge-intensive reasoning tasks. We propose Knowledge-Augmented Reasoning Distillation (KARD), a novel method that fine-tunes small LMs to generate rationales from LLMs with augmented knowledge retrieved from an external knowledge base. We empirically show that KARD significantly improves the performance of small T5 and GPT models on the challenging knowledge-intensive reasoning datasets.
  arXiv  Detail & Related papers  (2023-05-28T13:00:00Z)
• Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models [51.3422222472898]
  We document the capability of large language models (LLMs) like ChatGPT to predict stock price movements using news headlines. We develop a theoretical model incorporating information capacity constraints, underreaction, limits-to-arbitrage, and LLMs.
  arXiv  Detail & Related papers  (2023-04-15T19:22:37Z)
• Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models [103.71308117592963]
  We present an algorithm for training self-destructing models leveraging techniques from meta-learning and adversarial learning. In a small-scale experiment, we show MLAC can largely prevent a BERT-style model from being re-purposed to perform gender identification.
  arXiv  Detail & Related papers  (2022-11-27T21:43:45Z)
• Data-Driven and SE-assisted AI Model Signal-Awareness Enhancement and Introspection [61.571331422347875]
  We propose a data-driven approach to enhance models' signal-awareness. We combine the SE concept of code complexity with the AI technique of curriculum learning. We achieve up to 4.8x improvement in model signal awareness.
  arXiv  Detail & Related papers  (2021-11-10T17:58:18Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)

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.