TZ-Container: protecting container from untrusted OS with ARM TrustZone

Hua, Zhichao; Yu, Yang; Gu, Jinyu; Xia, Yubin; Chen, Haibo; Zang, Binyu

doi:10.1007/s11432-019-2707-6

Cited by 13 publications

(3 citation statements)

References 23 publications

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“…Hardware-based isolation. Recent work [29], [44], [45], [46], [47], [48], [49] explores new hardware to implement additional isolation for containers. SCONE [44] and ARMlock [47] place the container inside trusted execution domain based on the Intel SGX and ARM TrustZone.…”

Section: Related Workmentioning

confidence: 99%

vKernel: Enhancing Container Isolation via Private Code and Data

Huang,

Wang,

Rao

et al. 2024

IEEE Trans. Comput.

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Container technology is increasingly adopted in cloud environments. However, the lack of isolation in the shared kernel becomes a significant barrier to the wide adoption of containers. The challenges lie in how to simultaneously attain high performance and isolation. On the one hand, kernel-level isolation mechanisms, such as seccomp, capabilities, and apparmor, achieve good performance without much overhead, but lack the support for per-container customization. On the other hand, user-level and VM-based isolation offer superior security guarantees and allow for customization since a container is assigned a dedicated kernel, however, at the cost of high overhead. We present vKernel, a kernel isolation framework. It maintains a minimal set of code and data that are either sensitive or are prone to interference in a virtual kernel instance (vKI). vKernel relies on inline hooks to intercept and redirect requests sent to the host kernel to a vKI, where container-specific security rules, functions, and data are implemented. Through case studies, we demonstrate that under vKernel user-defined data isolation and kernel customization can be supported with a reasonable engineering effort. An evaluation of vKernel with micro-benchmarks, cloud services, real-world applications show that vKernel achieves good security guarantees, but with much less overhead.

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Section: Related Workmentioning

confidence: 99%

vKernel: Enhancing Container Isolation via Private Code and Data

Huang,

Wang,

Rao

et al. 2024

IEEE Trans. Comput.

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“…Arm confidential computing. Studies [32], [44], [48], [53], [75] have leveraged Arm TrustZone to simulate confidential computing environments in normal world and secure world. However, these security features are vulnerable to the secure world attacker [35] and new yet-unrealized adversaries in Arm CCA.…”

Section: Related Workmentioning

confidence: 99%

CAGE: Complementing Arm CCA with GPU Extensions

University),

Zhang,

Deng

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

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Confidential computing is an emerging technique that provides users and third-party developers with an isolated and transparent execution environment. To support this technique, Arm introduced the Confidential Computing Architecture (CCA), which creates multiple isolated address spaces, known as realms, to ensure data confidentiality and integrity in securitysensitive tasks. Arm recently proposed the concept of confidential computing on GPU hardware, which is widely used in generalpurpose, high-performance, and artificial intelligence computing scenarios. However, hardware and firmware supporting confidential GPU workloads remain unavailable. Existing studies leverage Trusted Execution Environments (TEEs) to secure GPU computing on Arm-or Intel-based platforms, but they are not suitable for CCA's realm-style architecture, such as using incompatible hardware or introducing a large trusted computing base (TCB). Therefore, there is a need to complement existing Arm CCA capabilities with GPU acceleration.To address this challenge, we present CAGE to support confidential GPU computing for Arm CCA. By leveraging the existing security features in Arm CCA, CAGE ensures data security during confidential computing on unified-memory GPUs, the mainstream accelerators in Arm devices. To adapt the GPU workflow to CCA's realm-style architecture, CAGE proposes a novel shadow task mechanism to manage confidential GPU applications flexibly. Additionally, CAGE leverages the memory isolation mechanism in Arm CCA to protect data confidentiality and integrity from the strong adversary. Based on this, CAGE also optimizes security operations in memory isolation to mitigate performance overhead. Without hardware changes, our approach uses the generic hardware security primitives in Arm CCA to defend against a privileged adversary. We present two prototypes to verify CAGE's functionality and evaluate performance, respectively. Results show that CAGE effectively provides GPU support for Arm CCA with an average of 2.45% performance overhead.

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“…Instead of adapting the entire driver into LDR, we imagine that such driver be divided into cooperative modules and only the security-critical modules such as resource manager be adapted into LDR while leaving modules responsible for user interaction and task scheduling inside the NW Linux kernel. In fact, such design choices have been investigated among GPUs [61], NICs [62] and even middlewares like VMM [63], container managers [64], etc. Considering the security-critical modules are more selfcontained, LDR can provide excellent runtime environment for these security-critical modules.…”

Section: B Ldr With More Complex Devicesmentioning

confidence: 99%

LDR: Secure and Efficient Linux Driver Runtime for Embedded TEE Systems

Yan,

Ling,

et al. 2024

Proceedings 2024 Network and Distributed System Security Symposium

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Trusted execution environments (TEEs), like Trust-Zone, are pervasively employed to protect security sensitive programs and data from various attacks. We target compact TEE operating systems like OP-TEE, which implement minimum TEE internal core APIs. Such a TEE OS often has poor device driver support and we want to alleviate such issue by reusing existing Linux drivers inside TEE OSes. An intuitive approach is to port all its dependency functions into the TEE OS so that the driver can directly execute inside the TEE. But this approach significantly enlarges the trusted computing base (TCB), making the TEE OS no longer compact. In this paper, we propose a TEE driver execution environment-Linux driver runtime (LDR). A Linux driver needs two types of functions, library functions and Linux kernel subsystem functions that a compact TEE OS does not have. The LDR reuses the existing TEE OS library functions whenever possible and redirects the kernel subsystem function calls to the Linux kernel in the normal world. LDR is realized as a sandbox environment, which confines the Linux driver inside the TEE through the ARM domain access control features to address associated security issues. The sandbox mediates the driver's TEE functions calls, sanitizing arguments and return values as well as enforcing forward control flow integrity. We implement and deploy an LDR prototype on an NXP IMX6Q SABRE-SD evaluation board, adapt 6 existing Linux drivers into LDR, and evaluate their performance. The experimental results show that the LDR drivers can achieve comparable performance with their Linux counterparts with negligible overheads. We are the first to reuse functions in both the TEE OS and normal world Linux kernel to run a TEE device driver and address related security issues.

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TZ-Container: protecting container from untrusted OS with ARM TrustZone

Cited by 13 publications

References 23 publications

vKernel: Enhancing Container Isolation via Private Code and Data

vKernel: Enhancing Container Isolation via Private Code and Data

CAGE: Complementing Arm CCA with GPU Extensions

LDR: Secure and Efficient Linux Driver Runtime for Embedded TEE Systems

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