arXiv:2605.29059v1 Announce Type: cross Abstract: Smart contract decompilation aims to recover high-level source code from bytecode, but evaluating decompilers remains difficult because existing studies use narrow datasets, inconsistent metrics, and limited semantic consistency checks. This gap is increasingly important as large language models (LLMs) begin to generate source-like Solidity that may compile and appear plausible, even when its semantics diverge from the original contract. We introduce SCDBench, a dataset and benchmark methodology for LLM-based smart contract decompilation. The d
The rapid advancement of LLMs necessitates robust evaluation methodologies for their application in critical areas like smart contract decompilation, where subtle errors can lead to significant financial loss. This work addresses a critical gap as LLMs are increasingly used to generate smart contract code.
A reliable benchmark for LLM-based smart contract decompilers will enhance security, auditability, and development efficiency within the burgeoning blockchain ecosystem. It directly impacts the safety and trustworthiness of decentralized applications and the underlying financial infrastructure.
The introduction of SCDBench provides a standardized and more comprehensive framework for evaluating LLM performance in a sensitive domain, enabling better comparison of decompiler tools and driving improvements in their accuracy and semantic consistency. This will lead to more secure smart contract development and auditing practices.
- · Blockchain developers
- · Smart contract auditors
- · LLM developers (security)
- · Decentralized finance (DeFi)
- · Malicious actors (exploiters)
- · Inefficient smart contract auditing firms
- · Undifferentiated LLM providers
Improved accuracy and reliability of smart contract decompilation using LLMs will lead to more secure and auditable blockchain applications.
Increased trust in LLM-generated or analyzed smart contracts could accelerate the adoption of blockchain technology across various industries.
The development of highly reliable LLM-based security tools could lower the barrier to entry for smart contract development while simultaneously raising the standard for security audits.
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Read at arXiv cs.AI