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Preprint (Author's original)CC BY V4.0, Open
Preprint
FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generation
arXiv (Cornell University)
Cornell University
22/10/2025
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Abstract
Decompilation converts machine code into human-readable form, enabling analysis and debugging without source code. However, fidelity issues often degrade the readability and semantic accuracy of decompiled output. Existing methods, such as variable renaming or structural simplification, provide partial improvements but lack robust detection and correction, particularly for complex closed-source binaries. We present FidelityGPT, a framework that enhances decompiled code accuracy and readability by systematically detecting and correcting semantic distortions. FidelityGPT introduces distortion-aware prompt templates tailored to closed-source settings and integrates Retrieval-Augmented Generation (RAG) with a dynamic semantic intensity algorithm to locate distorted lines and retrieve semantically similar code from a database. A variable dependency algorithm further mitigates long-context limitations by analyzing redundant variables and integrating their dependencies into the prompt context. Evaluated on 620 function pairs from a binary similarity benchmark, FidelityGPT achieved an average detection accuracy of 89% and a precision of 83%. Compared to the state-of-the-art DeGPT (Fix Rate 83%, Corrected Fix Rate 37%), FidelityGPT attained 94% FR and 64% CFR, demonstrating significant gains in accuracy and readability. These results highlight its potential to advance LLM-based decompilation and reverse engineering.
Details
- Title
- FidelityGPT: Correcting Decompilation Distortions with Retrieval Augmented Generation
- Creators
- Zhiping Zhou - Tianjin UniversityXiaohong Li - Tianjin UniversityRuitao Feng - Southern Cross UniversityYao Zhang - Tianjin UniversityYuekang Li - University of New South Wales (Australia, Sydney)Wenbu Feng - Tianjin UniversityYunqian Wang - Tianjin UniversityYuqing Li - Tianjin University
- Publication Details
- arXiv (Cornell University)
- Publisher
- Cornell University
- Identifiers
- 991013325410902368
- Academic Unit
- Faculty of Science and Engineering
- Language
- English
- Resource Type
- Preprint