Accessible Smart Contracts Verification: Synthesizing Formal Models with Tamed LLMs

TL;DR

This paper introduces a method to automate the creation of formal models for smart contracts using large language models, reducing time and expertise needed for verification and increasing accessibility of formal methods.

Contribution

It presents a novel three-phase model synthesis approach combining transpilation, LLM-based filling, and iterative repair to streamline formal model creation.

Findings

Reduces time to generate formal models for smart contracts.

Increases accessibility of formal verification methods.

Improves accuracy of models through iterative repair.

Abstract

When blockchain systems are said to be trustless, what this really means is that all the trust is put into software. Thus, there are strong incentives to ensure blockchain software is correct -- vulnerabilities here cost millions and break businesses. One of the most powerful ways of establishing software correctness is by using formal methods. Approaches based on formal methods, however, induce a significant overhead in terms of time and expertise required to successfully employ them. Our work addresses this critical disadvantage by automating the creation of a formal model -- a mathematical abstraction of the software system -- which is often a core task when employing formal methods. We perform model synthesis in three phases: we first transpile the code into model stubs; then we "fill in the blanks" using a large language model (LLM); finally, we iteratively repair the generated model, on both syntactical and semantical level. In this way, we significantly reduce the amount of time necessary to create formal models and increase accessibility of valuable software verification methods that rely on them. The practical context of our work was reducing the time-to-value of using formal models for correctness audits of smart contracts.