GPUs have become an integral part of modern systems, but their implications for system security are not yet clear. This paper demonstrates both that discrete GPUs cannot be used as secure co-processors and that GPUs provide a stealthy platform for malware. First, we examine a recent proposal to use discrete GPUs as secure co-processors and show that the security guarantees of the proposed system do not hold on the GPUs we investigate. Second, we demonstrate that (under certain circumstances) it is possible to bypass IOMMU protections and create stealthy, long-lived GPU-based malware. We demonstrate a novel attack that compromises the in-kernel GPU driver and one that compromises GPU microcode to gain full access to CPU physical memory. In general, we find that the highly sophisticated, but poorly documented GPU hardware architecture, hidden behind obscure close-source device drivers and vendor-specific APIs, not only make GPUs a poor choice for applications requiring strong security, but also make GPUs into a security threat.

Users of modern data-processing services such as tax preparation or genomic screening are forced to trust them with data that the users wish to keep secret. Ryoan 1 protects secret data while it is processed by services that the data owner does not trust. Accomplishing this goal in a distributed setting is difficult, because the user has no control over the service providers or the computational platform. Confining code to prevent it from leaking secrets is notoriously difficult, but Ryoan benefits from new hardware and a request-oriented data model. Ryoan provides a distributed sandbox, leveraging hardware enclaves (e.g., Intel’s software guard extensions (SGX) [40]) to protect sandbox instances from potentially malicious computing platforms. The protected sandbox instances confine untrusted data-processing modules to prevent leakage of the user’s input data. Ryoan is designed for a request-oriented data model, where confined modules only process input once and do not persist state about the input. We present the design and prototype implementation of Ryoan and evaluate it on a series of challenging problems including email filtering, health analysis, image processing and machine translation.

We introduce TxFS, a transactional file system that builds upon a file system's atomic-update mechanism such as journaling. Though prior work has explored a number of transactional file systems, TxFS has a unique set of properties: a simple API, portability across different hardware, high performance, low complexity (by building on the file-system journal), and full ACID transactions. We port SQLite, OpenLDAP, and Git to use TxFS and experimentally show that TxFS provides strong crash consistency while providing equal or better performance.

Serverless platforms have been attracting applications from traditional platforms because infrastructure management responsibilities are shifted from users to providers. Many applications well-suited to serverless environments could leverage GPU acceleration to enhance their performance. Unfortunately, current serverless platforms do not expose GPUs to serverless applications.