理解基于混淆的端侧LLM保护的安全边界

Computer Science > Cryptography and Security arXiv:2609.10117 (cs) [Submitted on 9 Sep 2026] Title:Understanding the Security Boundary of Obfuscation-based On-Device LLM Protection Authors:Hanyi Zhou, Chenyang Li, Yuanzhe Pang, Ke Xu, Mingwei Xu, Zhuotao Liu View a PDF of the paper titled Understanding the Security Boundary of Obfuscation-based On-Device LLM Protection, by Hanyi Zhou and 5 other authors View PDF HTML (experimental) Abstract:Trusted Execution Environments (TEEs) offer a promising mechanism for safeguarding the intellectual property of on-device Large Language Models (LLMs). To overcome the inherent computational bottlenecks of TEEs, existing TEE-Shielded LLM Partition (TSLP) methods apply efficient obfuscation schemes to computationally intensive layers, offloading them to external GPUs while retaining only lightweight operations within the TEE. Although a growing body of TSLP-based approaches has emerged, these defense mechanisms remain largely heuristic. Consequently, some methods are proven vulnerable to certain specialized adversarial attacks designed to exploit their specific architectural implementations. To overcome the limitations of these heuristic designs, this paper addresses a fundamental research question: can we establish common primitives to unify representative prior methodologies, characterize the security boundary of their compositions, and systematically extend them? To this end, we formalize a set of obfuscation primitives, defined as dual-tuples of linear computations satisfying specific algebraic properties. We demonstrate that the matrix-level weight transformations of the representative efficient TSLP frameworks studied in this paper can be expressed as compositions of these primitives; consequently, the canonical form of these primitive compositions, denoted as O_prior, characterizes the structural boundary of this primitive family. We then expose the vulnerabilities of O_prior through a novel primitive-guided attack methodology, Collapse, demonstrating a shared vulnerability in several prominent TSLP methods published in top-tier venues, such as ArrowCloak (Security'25), TSQP (S&P'25), and LoRO (NeurIPS'25). Finally, we introduce two novel obfuscation primitives and integrate them with existing constructs to formulate O_ext, extending this security boundary. Comments: Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2609.10117 [cs.CR] (or arXiv:2609.10117v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.10117 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hanyi Zhou [view email] [v1] Wed, 9 Sep 2026 12:51:55 UTC (1,746 KB) Full-text links: Access Paper: View a PDF of the paper titled Understanding the Security Boundary of Obfuscation-based On-Device LLM Protection, by Hanyi Zhou and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR new | recent | 2026-09 Change to browse by: cs 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?)