A Survey of Published Attacks on Intel SGX

Nilsson, Alexander; Bideh, Pegah Nikbakht; Brorsson, Joakim

doi:10.48550/arxiv.2006.13598

Cited by 6 publications

(6 citation statements)

References 39 publications

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“…• Defense mechanisms for SGX Alexander et al [28] summarized the available defense mechanisms for attacks on SGX at four levels: microcode patching, system design, compiler and SDK, and application layer design. CPU vendors can fix many security vulnerabilities through microcode patching.…”

Section: Defense Mechanisms For Teesmentioning

confidence: 99%

Survey of research on confidential computing

Feng,

Qin,

Feng

et al. 2024

IET Communications

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As the global data strategy deepens and data elements accelerate integrating and flowing more rapidly, the demand for data security and privacy protection has become increasingly prominent. Confidential computing emerges as a crucial security technology to solve security and privacy problem, and it is also a hot subject of in contemporary security technologies. Leveraging collaborative security in both hardware and software, it builds a trusted execution environment to ensure confidentiality and integrity protection for data in use. This paper provides a comprehensive overview of the development process of confidential computing, summarizing its current research status and issues, which focuses on the security requirements for data security and privacy protection. Furthermore, it deeply analyses the common technical features of confidential computing, and proposes a trusted confidential computing architecture based on collaborative hardware and software trust. Then, it elaborates on the research status and issues of confidential computing from four aspects: hardware security, architecture and key technologies, applications, and standards and evaluation. Finally, this paper provides a synthesis and outlook for the future development of confidential computing. In summary, confidential computing is currently in a rapidly developing stage and will play an important role in cyber security in the future.

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Section: Defense Mechanisms For Teesmentioning

confidence: 99%

Survey of research on confidential computing

Feng,

Qin,

Feng

et al. 2024

IET Communications

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“…In our current prototype implementation, we use Intel SGX enclaves as a TEE implementation target. SGX is vulnerable to a wide category of attacks reviewed in [24]. Chuchotage can be vulnerable to any attacks applicable to SGX.…”

Section: Security Evaluationmentioning

confidence: 99%

Chuchotage: In-line Software Network Protocol Translation for (D)TLS

Bideh

Paladi

2022

Information and Communications Security

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The growing diversity of connected devices leads to complex network deployments, often made up of endpoints that implement incompatible network application protocols. Communication between heterogeneous network protocols was traditionally enabled by hardware translators or gateways. However, such solutions are increasingly unfit to address the security, scalability, and latency requirements of modern software-driven deployments. To address these shortcomings we propose Chuchotage, a protocol translation architecture for secure and scalable machine-to-machine communication. Chuchotage enables in-line TLS interception and confidential protocol translation for software-defined networks. Translation is done in ephemeral, flow-specific Trusted Execution Environments and scales with the number of network flows. Our evaluation of Chuchotage implementing an HTTP to CoAP translation indicates a minimal transmission and translation overhead, allowing its integration with legacy or outdated deployments.

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“…A TEE leverages a root of trust from the hardware to shield the execution of sensitive computation and its data from the rest of the node, including higher privileged software layers. While TEEs have been proposed to make ML computation trustworthy [42,65,79,80], past TEE implementations have been shown to exhibit exploitable security vulnerabilities [12,78,84,111].…”

Section: Establishing Trustworthinessmentioning

confidence: 99%

“…Slalom [109] and Privado [42] use trusted execution environment (TEEs) [22] to produce trustworthy inference. They rely on the security of TEEs, which have suffered from successful attacks [12,78,84,111]. These solutions are restricted to CPU-based inference, which increases latencies by several orders of magnitude compared to GPUs.…”

Section: Related Workmentioning

confidence: 99%

Dropbear: Machine Learning Marketplaces made Trustworthy with Byzantine Model Agreement

Shamis¹,

Pietzuch²,

Delignat-Lavaud³

et al. 2022

Preprint

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Marketplaces for machine learning (ML) models are emerging as a way for organizations to monetize models. They allow model owners to retain control over hosted models by using cloud resources to execute ML inference requests for a fee, preserving model confidentiality. Clients that rely on hosted models require trustworthy inference results, even when models are managed by third parties. While the resilience and robustness of inference results can be improved by combining multiple independent models, such support is unavailable in today's marketplaces.We describe Dropbear, the first ML model marketplace that provides clients with strong integrity guarantees by combining results from multiple models in a trustworthy fashion. Dropbear replicates inference computation across a model group, which consists of multiple cloud-based GPU nodes belonging to different model owners. Clients receive inference certificates that prove agreement using a Byzantine consensus protocol, even under model heterogeneity and concurrent model updates. To improve performance, Dropbear batches inference and consensus operations separately: it first performs the inference computation across a model group, before ordering requests and model updates. Despite its strong integrity guarantees, Dropbear's performance matches that of state-ofthe-art ML inference systems: deployed across 3 cloud sites, it handles 800 requests/s with ImageNet models.

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A Survey of Published Attacks on Intel SGX

Cited by 6 publications

References 39 publications

Survey of research on confidential computing

Survey of research on confidential computing

Chuchotage: In-line Software Network Protocol Translation for (D)TLS

Dropbear: Machine Learning Marketplaces made Trustworthy with Byzantine Model Agreement

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