Briefing
The fundamental problem in auditing deployed smart contracts is the semantic loss incurred when decompiling low-level EVM bytecode back into a high-level representation, which severely compromises the efficiency and accuracy of formal verification tools. This research introduces SmartHalo , a novel framework that integrates static analysis and Large Language Models (LLMs) to overcome this barrier. The core breakthrough is the creation of a Dependency Graph (DG) , a precise data structure derived from static analysis, which is then used to prompt an LLM to accurately recover lost semantic information like variable types and function boundaries.
This enriched, high-fidelity output is subsequently validated via symbolic execution and formal verification, fundamentally transforming the process from a probabilistic audit to a mathematically rigorous proof of correctness. This innovation makes formal verification a practical, scalable defense for the vast and complex landscape of existing on-chain assets.
Context
The prevailing theoretical limitation in smart contract security is the difficulty of achieving comprehensive, sound formal verification for contracts already deployed on the Ethereum Virtual Machine (EVM). While formal methods provide mathematical guarantees of correctness, they rely on accurate, high-level code specifications. Existing decompilers produce semantically poor output from bytecode, forcing auditors to manually reconstruct complex control and data flow, which is time-intensive and error-prone. This bottleneck has confined formal verification primarily to greenfield development, leaving the majority of high-value, deployed contracts vulnerable to subtle, unverified logic flaws.
Analysis
The SmartHalo framework’s core mechanism is the synergistic combination of two distinct analytical techniques → rigorous static analysis and advanced semantic prediction via LLMs. The process begins by applying static analysis to the raw EVM bytecode to construct a Dependency Graph (DG) , which accurately maps all control and data flow relationships. This DG, which captures the underlying structure with mathematical soundness, serves as a high-quality, structured prompt for a Large Language Model. The LLM then leverages its vast training to perform Semantic Recovery , inferring and annotating high-level concepts such as variable names, complex data structures, and function attributes that were lost during the initial compilation.
Finally, the LLM-enhanced code is subjected to symbolic execution and formal verification using an SMT solver. This unique integration ensures the output is not only semantically rich and human-readable, but also mathematically provable against the original bytecode’s behavior, establishing a sound bridge between low-level execution and high-level logic.
Parameters
- Precision for Function Boundaries → 91.32% (The accuracy of the SmartHalo framework, when integrated with GPT-4o mini, in correctly identifying the start and end points of functions in the decompiled code.)
- Recall for Function Boundaries → 87.38% (The percentage of all true function boundaries that the SmartHalo framework successfully identified in the evaluation set.)
- Evaluation Dataset Size → 465 (The total number of randomly selected smart contract functions used to benchmark the performance of the SmartHalo framework.)
Outlook
This foundational research opens a critical new avenue for scaling security across the entire decentralized ecosystem. In the next three to five years, frameworks like SmartHalo are poised to be integrated directly into automated auditing platforms, enabling continuous, on-chain formal verification of existing protocols. The next steps for the academic community involve refining the Dependency Graph construction for non-linear constraints and exploring specialized, smaller LLMs fine-tuned exclusively for EVM semantics. This work fundamentally shifts the security model from reactive bug-hunting to proactive, mathematically guaranteed correctness, unlocking the potential for trillions in value to be secured by verifiable assurance, not just probabilistic testing.
