SoK：AI增强的二进制逆向

Computer Science > Cryptography and Security arXiv:2606.17398 (cs) [Submitted on 16 Jun 2026 (v1), last revised 4 Sep 2026 (this version, v2)] Title:SoK: AI-Augmented Binary Reversing Authors:Yujeong Kwon, Yiyue Zhang, Kexin Pei, Dokyung Song, Hyungjoon Koo View a PDF of the paper titled SoK: AI-Augmented Binary Reversing, by Yujeong Kwon and 4 other authors View PDF HTML (experimental) Abstract:Binary reversing is fundamental to software understanding, vulnerability discovery, malware investigation, and firmware auditing. However, it remains inherently challenging due to the lossy transformation of semantic information during compilation. Recent advances in machine learning, large language models (LLMs), and agentic AI systems have accelerated the adoption of AI-augmented binary reversing. Yet, the resulting body of work has become increasingly fragmented across reversing domains, artifact representations, learning approaches, and evaluation practices. This paper presents the first comprehensive systematization of knowledge on AI-augmented binary reversing. We collect 246 research papers published since 2015, and organize them into 22 binary reversing domains according to the inference tasks. We further introduce a unified taxonomy spanning conventional and AI-augmented reversing pipelines. Our taxonomy connects traditional analysis techniques, binary-derived artifacts, representation strategies, learning paradigms, and downstream inference tasks, while clarifying the emerging roles of LLMs and agentic AI systems. By establishing a common vocabulary and structured framework, we offer a holistic view of the field's evolution over the past decade. Our study reveals common structures underlying seemingly disparate approaches, highlights persistent technical challenges and evaluation gaps, and identifies promising opportunities for future research. Collectively, these insights clarify the current state of the field and provide a foundation for the next generation of evidence-grounded and practically deployable AI-augmented binary reversing systems. Comments: 21 pages, 7 tables, 4 figures Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) ACM classes: D.4.6; K.6.5 Cite as: arXiv:2606.17398 [cs.CR] (or arXiv:2606.17398v2 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2606.17398 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yujeong Kwon [view email] [v1] Tue, 16 Jun 2026 01:14:49 UTC (673 KB) [v2] Fri, 4 Sep 2026 11:26:45 UTC (872 KB) Full-text links: Access Paper: View a PDF of the paper titled SoK: AI-Augmented Binary Reversing, by Yujeong Kwon and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR new | recent | 2026-06 Change to browse by: cs cs.AI cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: 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?)