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SmartLLM: Smart Contract Auditing using Custom Generative AI

Jun Kevin, Pujianto Yugopuspito

TL;DR

SmartLLM tackles smart contract vulnerability detection by fusing Retrieval-Augmented Generation with domain knowledge from ERC standards and fine-tuning LLaMA 3.1 via QLoRA. The approach surpasses static analyzers Mythril/Slither and zero-shot LLM prompts, achieving 100% recall and 70.0% accuracy, with a F1 of 76.9% on a vulnerability dataset including reentrancy and access-control issues. By incorporating Detector, Reasoner, and Verificator roles and ERC-20 corpus validation, the method enhances accuracy and interpretability in auditing. This framework suggests a scalable, robust path toward more reliable DeFi security, though challenges in precision, long-contract processing, and computational cost remain for future work.

Abstract

Smart contracts are essential to decentralized finance (DeFi) and blockchain ecosystems but are increasingly vulnerable to exploits due to coding errors and complex attack vectors. Traditional static analysis tools and existing vulnerability detection methods often fail to address these challenges comprehensively, leading to high false-positive rates and an inability to detect dynamic vulnerabilities. This paper introduces SmartLLM, a novel approach leveraging fine-tuned LLaMA 3.1 models with Retrieval-Augmented Generation (RAG) to enhance the accuracy and efficiency of smart contract auditing. By integrating domain-specific knowledge from ERC standards and employing advanced techniques such as QLoRA for efficient fine-tuning, SmartLLM achieves superior performance compared to static analysis tools like Mythril and Slither, as well as zero-shot large language model (LLM) prompting methods such as GPT-3.5 and GPT-4. Experimental results demonstrate a perfect recall of 100% and an accuracy score of 70%, highlighting the model's robustness in identifying vulnerabilities, including reentrancy and access control issues. This research advances smart contract security by offering a scalable and effective auditing solution, supporting the secure adoption of decentralized applications.

SmartLLM: Smart Contract Auditing using Custom Generative AI

TL;DR

SmartLLM tackles smart contract vulnerability detection by fusing Retrieval-Augmented Generation with domain knowledge from ERC standards and fine-tuning LLaMA 3.1 via QLoRA. The approach surpasses static analyzers Mythril/Slither and zero-shot LLM prompts, achieving 100% recall and 70.0% accuracy, with a F1 of 76.9% on a vulnerability dataset including reentrancy and access-control issues. By incorporating Detector, Reasoner, and Verificator roles and ERC-20 corpus validation, the method enhances accuracy and interpretability in auditing. This framework suggests a scalable, robust path toward more reliable DeFi security, though challenges in precision, long-contract processing, and computational cost remain for future work.

Abstract

Smart contracts are essential to decentralized finance (DeFi) and blockchain ecosystems but are increasingly vulnerable to exploits due to coding errors and complex attack vectors. Traditional static analysis tools and existing vulnerability detection methods often fail to address these challenges comprehensively, leading to high false-positive rates and an inability to detect dynamic vulnerabilities. This paper introduces SmartLLM, a novel approach leveraging fine-tuned LLaMA 3.1 models with Retrieval-Augmented Generation (RAG) to enhance the accuracy and efficiency of smart contract auditing. By integrating domain-specific knowledge from ERC standards and employing advanced techniques such as QLoRA for efficient fine-tuning, SmartLLM achieves superior performance compared to static analysis tools like Mythril and Slither, as well as zero-shot large language model (LLM) prompting methods such as GPT-3.5 and GPT-4. Experimental results demonstrate a perfect recall of 100% and an accuracy score of 70%, highlighting the model's robustness in identifying vulnerabilities, including reentrancy and access control issues. This research advances smart contract security by offering a scalable and effective auditing solution, supporting the secure adoption of decentralized applications.

Paper Structure

This paper contains 18 sections, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. ERC-20
  4. Static Code Analysis
  5. Generative AI
  6. LLaMA 3.1
  7. QLoRA: Efficient Finetuning of Quantized LLMs
  8. Research Methodology
  9. Data Collection
  10. Data Preprocessing
  11. Model Fine-Tuning with QLoRA
  12. Instruction Scenarios
  13. Evaluation Metrics
  14. Results and Discussion
  15. Experimental Setup
  16. ...and 3 more sections

Figures (4)

  • Figure 1: How QLORA quantizing the model to 4-bit precision and using paged optimizers to handle memory spikes dettmers2023qlora.
  • Figure 2: Workflow diagram of SmartLLM, illustrating the roles of Detector, Reasoner, and Verificator in vulnerability detection using Retrieval-Augmented Generation.
  • Figure 3: Confusion Matrix for Training Dataset
  • Figure 4: Confusion Matrix for Testing Dataset