Enclave-Aware Compartmentalization and Secure Sharing with Sirius

Abstract

Hardware-assisted trusted execution environments (TEEs) are critical building blocks of many modern applications. However, they have a one-way isolation model that introduces a semantic gap between a TEE and its outside world. This lack of information causes an ever-increasing set of attacks on TEE-enabled applications that exploit various insecure interactions with the host OSs, applications, or other enclaves. We introduce Sirius, the first compartmentalization framework that achieves strong isolation and secure sharing in TEE-assisted applications by controlling the dataflows within primary kernel objects (e.g. threads, processes, address spaces, files, sockets, pipes) in both the secure and normal worlds. Sirius replaces ad-hoc interactions in current TEE systems with a principled approach that adds strong inter- and intra-address space isolation and effectively eliminates a wide range of attacks. We evaluate Sirius on ARM platforms and find that it is lightweight ($\approx 15K$ LoC) and only adds $\approx 10.8\%$ overhead to enable TEE support on applications such as httpd, and improves the performance of existing TEE-enabled applications such as the Darknet ML framework and ARM's LibDDSSec by $0.05\%-5.6\%$.

References in corpus (10)

• SgxPectre Attacks: Stealing Intel Secrets from SGX Enclaves via Speculative Execution
• Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware
• DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
• Chiron: Privacy-preserving Machine Learning as a Service
• Malware Guard Extension: Using SGX to Conceal Cache Attacks
• SGX-LKL: Securing the Host OS Interface for Trusted Execution
• Privado: Practical and Secure DNN Inference with Enclaves
• Confidential Deep Learning: Executing Proprietary Models on Untrusted Devices
• EnclaveDom: Privilege Separation for Large-TCB Applications in Trusted Execution Environments
• $\mu$Tiles: Efficient Intra-Process Privilege Enforcement of Memory Regions

Cited by in corpus (2)

• Machine Learning with Confidential Computing: A Systematization of Knowledge
• The Endokernel: Fast, Secure, and Programmable Subprocess Virtualization