Systematic Survey of AIOS: A Secure and Trustworthy AI-Native Operating System

WU Qingbo; WU Fuhui; LIU Haitian; LIU Jun; LI Yingjun; NONG Junkang; DU Yuqi; WU Haoze; CHEN Longteng; WANG Jing

doi:10.12263/DZXB.20250763

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Systematic Survey of AIOS: A Secure and Trustworthy AI-Native Operating System

\‌Research Progress on Ubiquitous Operating Systems and Environments for Human\-Cyber\-Physical Integrated Scenarios | 更新时间：2026-07-30

- Systematic Survey of AIOS: A Secure and Trustworthy AI-Native Operating System
- ACTA ELECTRONICA SINICA Vol. 54, Issue 4, Pages: 1564-1591(2026)
- 作者机构：
  
  1.麒麟软件有限公司2030实验室，湖南长沙 410000
  2.武汉学院信息工程学院，湖北武汉 430212
  3.国防科技大学计算机学院，湖南长沙 410000
- 作者简介：
- 基金信息：
  
  Collaborative Education Project of the Ministry of Education(240800006162639;241001613173522)
- DOI：10.12263/DZXB.20250763
  CLC： TP18;
- Received：04 September 2025，
  
  Accepted：10 December 2025，
  
  Online First：29 January 2026，
  
  Published：25 April 2026
- 稿件说明：
移动端阅览
吴庆波, 伍复慧, 刘海天, 等. AIOS综述：安全可信的人工智能原生操作系统[J]. 电子学报, 2026, 54(04): 1564-1591.

WU Qingbo, WU Fuhui, LIU Haitian, et al. Systematic Survey of AIOS: A Secure and Trustworthy AI-Native Operating System[J]. Acta Electronica Sinica, 2026, 54(04): 1564-1591.
吴庆波, 伍复慧, 刘海天, 等. AIOS综述：安全可信的人工智能原生操作系统[J]. 电子学报, 2026, 54(04): 1564-1591. DOI：10.12263/DZXB.20250763

WU Qingbo, WU Fuhui, LIU Haitian, et al. Systematic Survey of AIOS: A Secure and Trustworthy AI-Native Operating System[J]. Acta Electronica Sinica, 2026, 54(04): 1564-1591. DOI：10.12263/DZXB.20250763

摘要

随着人工智能技术的快速发展，传统操作系统在安全性、智能性与场景适配性等方面面临新的挑战。本文系统梳理了安全可信的人工智能原生操作系统（AI-native Operating System，AIOS）的研究进展与关键技术。首先，设计AIOS的分层架构，包括内核组件库、资源管理服务、共性基础服务以及领域基础软件栈。面向资源、运维与安全的系统级智能化服务框架和围绕安全内核架构、软硬件协同的安全与适配框架，以及虚拟化支撑的纵深防御安全访问框架构成共性基础服务。通过标准化接口与内核/核心服务在调度、内存、I/O与安全域等方面向上提供领域基础软件栈接口（涵盖人工智能（Artificial Intelligence，AI）软件栈与安全软件栈），支撑行业场景落地。其次，提出人工智能原生操作系统的关键技术：端侧智能操作系统聚焦模型压缩、推理优化（如键值（Key-Value，KV）缓存与分页注意力）与端云协同的异构资源调度及硬件抽象与驱动适配；记忆管理系统围绕记忆存储、检索与管理，结合短期、长期记忆以支撑长上下文理解与动态更新；安全可信机制涵盖内存安全与形式化验证、自动化检测、可信启动与远程证明，以及基于可信执行环境（Trusted Execution Environment，TEE）、机密计算与虚拟化隔离的纵深防御。随后，结合应用场景，综述AIOS在具身智能、工业与移动等领域的部署路径、能力边界与工程权衡，分析端侧推理、实时控制、跨设备协同与多模态交互的实践挑战。进一步，围绕新型操作系统基础技术、智能服务框架与关键技术梳理及安全可信基石，总结架构模块化与软硬件协同、智能服务的资源调度与安全鲁棒性、记忆系统的效率与隐私保护、领域基础软件栈的标准化与生态建设，以及跨平台与跨设备的智能协同与资源共享等开放问题与趋势。本文贡献在于提供结构化视角、术语边界与技术地图，并对典型系统与工具进行对比归纳，为AIOS研究与工程落地提供参考。

Abstract

With the rapid development of artificial intelligence technology

traditional operating systems face new challenges in security

intelligence

and scenario adaptability. This survey systematically reviews the research progress and key technologies of secure and trustworthy AI-native operating systems (AIOS). First

it introduces the layered architecture of AIOS

including kernel components

resource management

general foundational services (covering both the intelligent service framework and the secure access framework)

and domain-specific software stacks

highlighting the deep integration of artificial intelligence (AI) capabilities with operating systems. The secure access framework focuses on secure kernel architecture

software-hardware collaborative security and adaptation

and virtualization-based in-depth defense. Second

it summarizes the key AIOS technologies: edge-oriented operating systems emphasize model compression

inference optimization (e.g.

key-value (KV) cache and PagedAttention)

edge-cloud collaboration with heterogeneous resource scheduling

and hardware abstraction and driver adaptation; the memory management system centers on memory storage

retrieval

and management

combining short-term and long-term memory to support long context understanding and dynamic updates; the security and trust mechanisms cover memory safety and formal verification

automated analysis

trusted boot and remote attestation

and defense-in-depth via trusted execution environment (TEE)/confidential computing and virtualization isolation. Finally

by examining practical scenarios such as embodied intelligence

industrial internet of things (IoT)

and mobile applications

it analyzes deployment paths

capability boundaries

and engineering trade-offs

and summarizes open issues and trends including architectural modularity

co-design of hardware and software

resource scheduling and security robustness in intelligent services

efficiency and privacy of memory systems

standardization and ecosystem building of domain stacks

and cross-platform/device collaboration and resource sharing. This survey aims to provide a structured perspective

clear terminology boundaries

and a technology map

offering references for AIOS research and engineering practice.

关键词

Keywords

references

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