AI 中文总结
本文提出CCAegis系统，扩展Arm CCA实现进程内隔离，仅信任安全监视器以最小化TCB，在真实密码工作负载中性能开销为1.01x-1.43x，可有效隔离敏感数据与操作。
AI 中文摘要
随着机密计算的日益普及，安全敏感应用常部署在机密虚拟机（CVM）中，这降低了对第三方云提供商的依赖。但操作系统（OS）发起的特权攻击仍是此类环境中的重大威胁。现有更细粒度的隔离方案（如SHELTER）虽提供进程级保护，却仍易受进程内攻击，且存在OS与进程内攻击者合谋的风险。当前许多进程内隔离技术仍依赖OS管理和实施隔离域，导致可信计算基（TCB）庞大。这一缺口凸显了对更细粒度、更少信任依赖的机密计算解决方案的需求。本文提出CCAegis系统，它扩展Arm机密计算架构（CCA）以强制敏感数据与操作的进程内隔离，保护其免受进程内攻击者和OS的侵害。我们采用静态分析追踪敏感数据流，识别处理此类数据的函数；在函数调用和返回点插入权限切换指令，通过颗粒保护表（GPT）调整权限，确保仅指定函数可访问隔离数据。值得注意的是，CCAegis仅信任安全监视器，由其配置GPT并管理域切换，从而最小化TCB。我们在官方模拟器和真实开发板上实现CCAegis以评估其性能，实验结果显示，在真实密码工作负载中，与原始版本相比，CCAegis的性能开销为1.01倍至1.43倍，可有效隔离敏感数据与操作。
英文摘要
With the increasing adoption of confidential computing, security-sensitive applications are often deployed in confidential virtual machines (CVMs), which reduce reliance on third-party cloud providers. However, privilege attacks originating from the OS remain a significant threat in these environments. Existing finer-grained isolation schemes, such as SHELTER, provide process-level protection but are still vulnerable to intraprocess attacks and potential collusion between the OS and intra-process adversaries. Many current intra-process isolation techniques continue to depend on the OS to manage and enforce isolation domains, leading to a large Trusted Computing Base (TCB). This gap highlights the need for more granular, less trust-dependent confidential computing solutions. In this paper, we present CCAegis, a system that extends the Arm Confidential Compute Architecture (CCA) to enforce intra-process isolation of sensitive data and operations, safeguarding them from both intraprocess adversaries and the OS. We employ static analysis to track the flow of sensitive data and identify functions that handle such data. Permission-switching instructions are inserted at the function call and return points, adjusting permissions via the Granule Protection Table (GPT) to ensure that only designated functions can access the isolated data. Notably, CCAegis places trust solely in the Secure Monitor, which configures the GPTs and manages domain switching, thereby minimizing the TCB. We implemented CCAegis on both an official emulator and a real development board to assess its performance. Our experimental results show that CCAegis effectively isolates sensitive data and operations, with performance overheads ranging from 1.01x to 1.43x compared to the original version across real-world cryptographic workloads.
CommentsPublished in IEEE Transactions on Information Forensics and Security (TIFS)
Journal refIEEE Transactions on Information Forensics and Security, vol. 20, pp. 12507-12522, 2025
