Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications

Core Concepts

TrustLLM combines fine-tuning and LLM-based agents to enhance smart contract auditing, achieving high accuracy in vulnerability detection.

Abstract

The content discusses TrustLLM, a framework that integrates fine-tuning and large language models (LLMs) for intuitive smart contract auditing. It addresses the limitations of existing models by proposing a two-stage approach involving Detector and Reasoner models. TrustLLM employs LLM-based agents, Ranker and Critic, to select optimal causes of vulnerabilities. The model outperforms traditional fine-tuned models and zero-shot learning LLMs in detecting vulnerabilities. Additionally, it provides detailed insights into data collection methods, training processes, evaluation metrics, and experimental results.

Introduction to Smart Contracts:

Smart contracts on blockchains like Ethereum are crucial for decentralized finance.
Vulnerabilities in smart contracts can lead to significant financial losses.

Role of Large Language Models (LLMs):

Recent research shows potential of LLMs in auditing smart contracts.
Existing off-the-shelf LLMs lack domain-specific fine-tuning for Solidity smart contract auditing.

TrustLLM Framework:

Combines fine-tuning with LLM-based agents for intuitive auditing.
Two-stage approach involves Detector for decisions and Reasoner for causes of vulnerabilities.

Data Collection and Enhancement:

Balanced dataset collected with positive and negative samples.
Data enhancement using GPT-4 to improve explanations of vulnerabilities.

Evaluation:

Performance comparison with baseline models shows TrustLLM's superiority in detection accuracy.
Explanation alignment analysis demonstrates TrustLLM's consistency with real reasons.

Further Experiments:

Majority voting enhances performance stability.
Additional call graph information does not significantly impact model performance.

Stats

Recent research has shown that even GPT-4 achieves only 30% precision in auditing smart contracts when both decision and justification are correct.
On a dataset of 263 real smart contract vulnerabilities, TrustLLM achieves an F1 score of 91.21% and an accuracy of 91.11%.

Quotes

"One of the big skills in bug bounties that’s really difficult to teach is intuition." — Katie Paxton-Fear

Key Insights Distilled From

Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications

by Wei Ma,Daoyu... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16073.pdf

Deeper Inquiries

How can TrustLLM's framework be applied beyond smart contract auditing?

TrustLLM's framework can be applied to various domains beyond smart contract auditing where there is a need for intuitive decision-making and explanation generation. For example:

Code Security: TrustLLM can be used to audit code for security vulnerabilities in software applications, ensuring robustness and reliability.
Compliance Checking: The framework can assist in verifying compliance with regulations and standards by analyzing code or documents for adherence to specific guidelines.
Medical Diagnosis: TrustLLM could aid in medical diagnosis by detecting patterns in patient data and providing explanations for potential health issues.
Legal Analysis: In the legal field, TrustLLM could help analyze legal documents, contracts, or case law to identify inconsistencies or potential risks.

What counterarguments exist against the effectiveness of combining fine-tuning with LLM-based agents?

Some counterarguments against the effectiveness of combining fine-tuning with LLM-based agents include:

Overfitting: Fine-tuning may lead to overfitting on the training data, resulting in reduced generalization performance on unseen data.
Complexity: The process of fine-tuning multiple models and coordinating them effectively may introduce complexity that hinders overall model performance.
Resource Intensive: Fine-tuning large language models requires significant computational resources and time, making it impractical for some applications.
Interpretability: Combining multiple models may reduce the interpretability of results, making it challenging to understand how decisions are made.

How might the concept of intuition play a role in other domains outside blockchain technology?

The concept of intuition can play a crucial role in various domains outside blockchain technology by enabling quick decision-making based on gut feelings or instinctive judgments rather than lengthy analysis:

Healthcare: Healthcare professionals often rely on intuition when diagnosing patients based on experience and pattern recognition.
Finance: Traders use intuition to make split-second decisions about investments based on market trends and their instincts about future outcomes.
Emergency Response: First responders trust their intuition during emergencies to make rapid decisions that save lives without extensive deliberation.
Creativity: Artists and designers often rely on intuition when creating new works as they follow their instincts rather than strict rules or guidelines.

These examples illustrate how intuition plays a vital role across diverse fields by complementing analytical reasoning with quick, instinctual responses based on expertise and experience.