Decompiling Smart Contracts with a Large Language Model

• URL: http://arxiv.org/abs/2506.19624v1
• Date: Tue, 24 Jun 2025 13:42:59 GMT
• Title: Decompiling Smart Contracts with a Large Language Model
• Authors: Isaac David, Liyi Zhou, Dawn Song, Arthur Gervais, Kaihua Qin,
• Abstract summary: Despite Etherscan's 78,047,845 smart contracts deployed on (as of May 26, 2025), a mere 767,520 ( 1%) are open source.<n>This opacity necessitates the automated semantic analysis of on-chain smart contract bytecode.<n>We introduce a pioneering decompilation pipeline that transforms bytecode into human-readable and semantically faithful Solidity code.
• Score: 51.49197239479266
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The widespread lack of broad source code verification on blockchain explorers such as Etherscan, where despite 78,047,845 smart contracts deployed on Ethereum (as of May 26, 2025), a mere 767,520 (< 1%) are open source, presents a severe impediment to blockchain security. This opacity necessitates the automated semantic analysis of on-chain smart contract bytecode, a fundamental research challenge with direct implications for identifying vulnerabilities and understanding malicious behavior. Prevailing decompilers struggle to reverse bytecode in a readable manner, often yielding convoluted code that critically hampers vulnerability analysis and thwarts efforts to dissect contract functionalities for security auditing. This paper addresses this challenge by introducing a pioneering decompilation pipeline that, for the first time, successfully leverages Large Language Models (LLMs) to transform Ethereum Virtual Machine (EVM) bytecode into human-readable and semantically faithful Solidity code. Our novel methodology first employs rigorous static program analysis to convert bytecode into a structured three-address code (TAC) representation. This intermediate representation then guides a Llama-3.2-3B model, specifically fine-tuned on a comprehensive dataset of 238,446 TAC-to-Solidity function pairs, to generate high-quality Solidity. This approach uniquely recovers meaningful variable names, intricate control flow, and precise function signatures. Our extensive empirical evaluation demonstrates a significant leap beyond traditional decompilers, achieving an average semantic similarity of 0.82 with original source and markedly superior readability. The practical viability and effectiveness of our research are demonstrated through its implementation in a publicly accessible system, available at https://evmdecompiler.com.

Related papers

• CodeBC: A More Secure Large Language Model for Smart Contract Code Generation in Blockchain [6.7475904578675285]
  Large language models (LLMs) excel at generating code from natural language instructions, yet they often lack an understanding of security vulnerabilities.<n>This limitation makes it difficult for LLMs to avoid security risks in generated code, particularly in high-security programming tasks such as smart contract development for blockchain.<n>We introduce CodeBC, a code generation model specifically designed for generating secure smart contracts in blockchain.
  arXiv  Detail & Related papers  (2025-04-28T14:14:16Z)
• Simplicity by Obfuscation: Evaluating LLM-Driven Code Transformation with Semantic Elasticity [4.458584890504334]
  Code obfuscation aims to prevent reverse engineering and intellectual property theft.<n>The recent development of large language models paves the way for practical applications in different domains.<n>This work performs an empirical study on the ability of LLMs to obfuscate Python source code.
  arXiv  Detail & Related papers  (2025-04-18T18:29:23Z)
• The Code Barrier: What LLMs Actually Understand? [7.407441962359689]
  This research uses code obfuscation as a structured testing framework to evaluate semantic understanding capabilities of language models.<n>Findings show a statistically significant performance decline as obfuscation complexity increases.<n>This research introduces a new evaluation approach for assessing code comprehension in language models.
  arXiv  Detail & Related papers  (2025-04-14T14:11:26Z)
• ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding [60.37988508851391]
  Language models (LMs) have become a staple of the code-writing toolbox.<n>Research exploring modifications to Code-LMs' pre-training objectives, geared towards improving data efficiency and better disentangling between syntax and semantics, has been noticeably sparse.<n>In this work, we examine grounding on obfuscated code as a means of helping Code-LMs look beyond the surface-form syntax and enhance their pre-training sample efficiency.
  arXiv  Detail & Related papers  (2025-03-27T23:08:53Z)
• TFHE-Coder: Evaluating LLM-agentic Fully Homomorphic Encryption Code Generation [10.597643264309415]
  Homomorphic Encryption over the torus (TFHE) enables encrypted computation on data without decryption.<n>Despite its potential in privacy preserving machine learning, secure multi party computation, private blockchain transactions, and secure medical diagnostics, its adoption remains limited due to cryptographic complexity and usability challenges.<n>This work establishes the first benchmark for TFHE code generation, demonstrating how LLMs, when augmented with domain-specific feedback, can bridge the expertise gap in FHE code generation.
  arXiv  Detail & Related papers  (2025-03-15T17:57:44Z)
• Combining GPT and Code-Based Similarity Checking for Effective Smart Contract Vulnerability Detection [0.0]
  We present SimilarGPT, a vulnerability identification tool for smart contract.<n>The main concept of SimilarGPT is to measure the similarity between the code under inspection and the secure code from third-party libraries.<n>We propose optimizing the detection sequence using topological ordering to enhance logical coherence and reduce false positives during detection.
  arXiv  Detail & Related papers  (2024-12-24T07:15:48Z)
• HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data [60.75578581719921]
  Large language models (LLMs) have shown great potential for automatic code generation. Recent studies highlight that many LLM-generated code contains serious security vulnerabilities. We introduce HexaCoder, a novel approach to enhance the ability of LLMs to generate secure codes.
  arXiv  Detail & Related papers  (2024-09-10T12:01:43Z)
• FoC: Figure out the Cryptographic Functions in Stripped Binaries with LLMs [51.898805184427545]
  We propose a novel framework called FoC to Figure out the Cryptographic functions in stripped binaries.<n>We first build a binary large language model (FoC-BinLLM) to summarize the semantics of cryptographic functions in natural language.<n>We then build a binary code similarity model (FoC-Sim) upon the FoC-BinLLM to create change-sensitive representations and use it to retrieve similar implementations of unknown cryptographic functions in a database.
  arXiv  Detail & Related papers  (2024-03-27T09:45:33Z)
• CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion [117.178835165855]
  This paper introduces CodeAttack, a framework that transforms natural language inputs into code inputs. Our studies reveal a new and universal safety vulnerability of these models against code input. We find that a larger distribution gap between CodeAttack and natural language leads to weaker safety generalization.
  arXiv  Detail & Related papers  (2024-03-12T17:55:38Z)
• A Transformer-based Approach for Source Code Summarization [86.08359401867577]
  We learn code representation for summarization by modeling the pairwise relationship between code tokens. We show that despite the approach is simple, it outperforms the state-of-the-art techniques by a significant margin.
  arXiv  Detail & Related papers  (2020-05-01T23:29:36Z)

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.