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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. 

The recent development of Trusted Execution Environment has brought unprecedented opportunities for confidential computing within cloud-based systems. Among various popular cloud business models, serverless computing has gained dominance since its emergence, leading to a high demand for confidential serverless computing services based on trusted enclaves. However, the issue of cold start overhead significantly hinders its performance, as new enclaves need to be created to ensure a clean and verifiable execution environment. In this paper, we propose a novel approach for constructing reusable enclaves that enable rapid enclave reset and robust security with three key enabling techniques: enclave snapshot and rewinding, nested attestation, and multi-layer intra-enclave compartmentalisation. We have built a prototype system for confidential serverless computing, integrating OpenWhisk and a WebAssembly runtime, which significantly reduces the cold start overhead in an end-to-end serverless setting while imposing a reasonable performance impact on standard execution. 

Digital contact tracing offers significant promise to help reduce the spread of SARS-CoV-2 and other viruses. Google and Apple joined together in 2020 to create the Google/Apple Exposure Notification (GAEN) framework to determine encounters with anonymous users later diagnosed COVID-19 positive. However, as GAEN lacks geospatial awareness, it is susceptible to geographically distributed replay attacks. Anonymous, low-cost, crowd-sourced replay attack networks deployed by malicious actors (or far away nation-state attackers) who utilize malicious (or innocent) users’ smartphones to capture and replay GAEN advertisements can drastically increase false-positive rates even in areas that otherwise exhibit low positivity rates. In response to this powerful replay attack, we introduce GAEN+ , a solution that enhances GAEN with geospatial awareness while maintaining user privacy, and demonstrate its ability to effectively prevent geographically distributed replay attacks.