The Road to Trust: Building Enclaves within Confidential VMs
<|reference_start|>The Road to Trust: Building Enclaves within Confidential VMs: Integrity is critical for maintaining system security, as it ensures that only genuine software is loaded onto a machine. Although confidential virtual machines (CVMs) function within isolated environments separate from the host, it is important to recognize that users still encounter challenges in maintaining control over the integrity of the code running within the trusted execution environments (TEEs). The presence of a sophisticated operating system (OS) raises the possibility of dynamically creating and executing any code, making user applications within TEEs vulnerable to interference or tampering if the guest OS is compromised. To address this issue, this paper introduces NestedSGX, a framework which leverages virtual machine privilege level (VMPL), a recent hardware feature available on AMD SEV-SNP to enable the creation of hardware enclaves within the guest VM. Similar to Intel SGX, NestedSGX considers the guest OS untrusted for loading potentially malicious code. It ensures that only trusted and measured code executed within the enclave can be remotely attested. To seamlessly protect existing applications, NestedSGX aims for compatibility with Intel SGX by simulating SGX leaf functions. We have also ported the SGX SDK and the Occlum library OS to NestedSGX, enabling the use of existing SGX toolchains and applications in the system. Performance evaluations show that context switches in NestedSGX take about 32,000 -- 34,000 cycles, approximately $1.33\times$ -- $1.54\times$ higher than that of Intel SGX. NestedSGX incurs minimal overhead in most real-world applications, with an average overhead below 2% for computation and memory intensive workloads and below 15.68% for I/O intensive workloads.<|reference_end|>
@article{wang2024the,
    title={The Road to Trust: Building Enclaves within Confidential VMs},
    author={Wenhao Wang, Linke Song, Benshan Mei, Shuang Liu, Shijun Zhao,
  Shoumeng Yan, XiaoFeng Wang, Dan Meng, Rui Hou},
    journal={arXiv preprint arXiv:2402.11438},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11438},
    primaryClass={cs.CR cs.AR}
}