Decoupling is a Necessity: Transformation-Agnostic Decompiled Code Recovery under Optimization and Obfuscation

[2608.27889] Decoupling is a Necessity: Transformation-Agnostic Decompiled Code Recovery under Optimization and Obfuscation Skip to main content Search Submit Donate Log in Computer Science > Software Engineering arXiv:2608.27889 (cs) [Submitted on 28 Aug 2026] Title: Decoupling is a Necessity: Transformation-Agnostic Decompiled Code Recovery under Optimization and Obfuscation Authors: Zhiping Zhou , Xiaohong Li , Ruitao Feng , Yao Zhang , Yuekang Li , Wenbu Feng View PDF HTML (experimental) Abstract: Reverse engineering is essential for software security analysis and vulnerability detection. Decompilation, the process of lifting binaries to high-level pseudocode, is central to this task. However, production binaries are hostile environments: aggressive compiler optimizations and adversarial obfuscation jointly mangle control structures, obscure variable intents, and disguise high-level program logic. Consequently, existing LLM-based decompilation tools frequently suffer from structural collapse and semantic hallucinations. We present ReSource, the first multi-phase LLM framework designed for transformation-agnostic source recovery. To tackle these intertwined distortions, ReSource conceptualizes the binary-to-source discrepancies into three orthogonal tiers, namely lexical, syntactic, and semantic, and decouples the recovery process accordingly. First, to ground the LLM and prevent logic drift, it retrieves empirical priors from a curated Semantic Distortion Database. Second, to resolve control-flow flattening, it integrates a lightweight predictor to reconstruct the source-level structural skeleton. Finally, a contextual lexical deduction stage refines identifiers to restore human readability. Evaluated on a massive benchmark of over 80,000 decompiled-source function pairs across three optimization levels and four obfuscation techniques, ReSource achieves an 83% Top-5 source retrieval accuracy and an average similarity score of 0.66. By maintaining robust semantic identifiability where state-of-the-art baselines (DeGPT, LLM4Decompile, and FidelityGPT) severely overfit or degrade, ReSource provides a scalable and reliable foundation for downstream security analysis. Comments: Preprint. 11 pages, 3 figures, 3 tables Subjects: Software Engineering (cs.SE) ACM classes: D.2.5; I.2.2; D.4.6 Cite as: arXiv:2608.27889 [cs.SE] (or arXiv:2608.27889v1 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2608.27889 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zhiping Zhou [ view email ] [v1] Fri, 28 Aug 2026 03:52:54 UTC (717 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.SE < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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