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  1. An increasing number of Trusted Execution Environment (TEE) is adopting to a variety of commercial products for protecting data security on the cloud. However, TEEs are still exposed to various side-channel vulnerabilities, such as execution order-based, timing-based, and power-based vulnerabilities. While recent hardware is applying various techniques to mitigate order-based and timing-based side-channel vulnerabilities, power-based side-channel attacks remain a concern of hardware security, especially for the confidential computing settings where the server machines are beyond the control of cloud users. In this paper, we present PWRLEAK, an attack framework that exploits AMD’s power reporting interfaces to build power side-channel attacks against AMD Secure Encrypted Virtualization (SEV)-protected VM. We design and implement the attack framework with three general steps: (1) identify the instruction running inside AMD SEV, (2) apply a power interpolator to amplify power consumption, including an emulation-based interpolator for analyzing purposes and a moregeneral interrupt-based interpolator, and (3) infer secrets with various analysis approaches. A case study of using the emulation-based interpolator to infer the whole JPEG images processed by libjpeg demonstrates its ability to help analyze power consumption inside SEV VM. Our end-to-end attacks against Intel’s Integrated Performance Primitives (Intel IPP) library indicates that PWRLEAK can be exploited to infer RSA private keys with over 80% accuracy using the interrupt based interpolator. 
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    Free, publicly-accessible full text available July 10, 2024
  2. Free, publicly-accessible full text available July 1, 2024
  3. Speculative-execution attacks, such as SgxSpectre, Foreshadow, and MDS attacks, leverage recently disclosed CPU hardware vulnerabilities and micro-architectural side channels to breach the confidentiality and integrity of Intel Software Guard eXtensions (SGX). Unlike traditional micro-architectural side-channel attacks, speculative-execution attacks extract any data in the enclave memory, which makes them very challenging to defeat purely from the software. However, to date, Intel has not completely mitigated the threats of speculative-execution attacks from the hardware. Hence, future attack variants may emerge. This paper proposes a software-based solution to speculative-execution attacks, even with the strong assumption that confidentiality of enclave memory is compromised. Our solution extends an existing work called HyperRace, which is a compiler-assisted tool for detecting Hyper-Threading based side-channel attacks against SGX enclaves, to thwart speculative-execution attacks from within SGX enclaves. It requires supports from the untrusted operating system, e.g., for temporarily disabling interrupts, but verifies the OS's behaviors. Additional microcode upgrades are required from Intel to secure the attestation flow. 
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  4. AMD’s Secure Encrypted Virtualization (SEV) is an emerging technology to secure virtual machines (VM) even in the presence of malicious hypervisors. However, the lack of trust in the privileged software also introduces an assortment of new attack vectors to SEV-enabled VMs that were mostly unexplored in the literature. This paper studies the insecurity of SEV from the perspective of the unprotected I/O operations in the SEV-enabled VMs. The results are alerting: not only have we discovered attacks that breach the confidentiality and integrity of these I/O operations—which we find very difficult to mitigate by existing approaches—but more significantly we demonstrate the construction of two attack primitives against SEV’s memory encryption schemes, namely a memory decryption oracle and a memory encryption oracle, which enables an adversary to decrypt and encrypt arbitrary messages using the memory encryption keys of the VMs. We evaluate the proposed attacks and discuss potential solutions to the underlying problems. 
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  5. Serverless computing is an emerging paradigm in which an application's resource provisioning and scaling are managed by third-party services. Examples include AWS Lambda, Azure Functions, and Google Cloud Functions. Behind these services' easy-to-use APIs are opaque, complex infrastructure and management ecosystems. Taking on the viewpoint of a serverless customer, we conduct the largest measurement study to date, launching more than 50,000 function instances across these three services, in order to characterize their architectures, performance, and resource management efficiency. We explain how the platforms isolate the functions of different accounts, using either virtual machines or containers, which has important security implications. We characterize performance in terms of scalability, coldstart latency, and resource efficiency, with highlights including that AWS Lambda adopts a bin-packing-like strategy to maximize VM memory utilization, that severe contention between functions can arise in AWS and Azure, and that Google had bugs that allow customers to use resources for free. 
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