A Study of Overflow Vulnerabilities on GPUs
GPU-accelerated computing gains rapidly-growing popularity in many areas such as scientific computing, database systems, and cloud environments. However, there are less investigations on the security implications of concurrently running GPU applications.
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Abstract. GPU-accelerated computing gains rapidly-growing popularity in many areas such as scientific computing, database systems, and cloud environments. However, there are less investigations on the security implications of concurrently running GPU applications. In this paper, we explore security vulnerabilities of CUDA from multiple dimensions. In particular, we first present a study on GPU stack, and reveal that stack overflow of CUDA can affect the execution of other threads by manipulating different memory spaces. Then, we show that the heap of CUDA is organized in a way that allows threads from the same warp or different blocks or even kernels to overwrite each other’s content, which indicates a high risk of corrupting data or steering the execution flow by overwriting function pointers. Furthermore, we verify that integer overflow and function pointer overflow in struct also can be exploited on GPUs. But other attacks against format string and exception handler seems not feasible due to the design choices of CUDA runtime and programming language features. Finally, we propose potential solutions of preventing the presented vulnerabilities for CUDA. Keywords: GPGPU
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· CUDA · Security · Buffer overflow
Introduction
Graphics processing units (GPUs) were originally developed to perform complex mathematical and geometric calculations that are indispensable parts of graphics rendering. Nowadays, due to the high performance and data parallelism, GPUs have been increasingly adopted to perform generic computational tasks. For example, GPUs can provide a significant speed-up for financial and scientific computations. GPUs also have been used to accelerate network traffic processing in software routers by offloading specific computations to GPUs. Computation-intensive encryption algorithms like AES have also been ported to GPU platforms to exploit the data parallelism, and significant improvement in throughput was reported. In addition, using GPUs for co-processing in database systems, such as offloading query processing to GPUs, has also been shown to be beneficial. With the remarkable success of adopting GPUs in a diverse range of realworld applications, especially the flourish of cloud computing and advancement c IFIP International Federation for Information Processing 2016 Published by Springer International Publishing AG 2016. All Rights Reserved G.R. Gao et al. (Eds.): NPC 2016, LNCS 9966, pp. 103–115, 2016. DOI: 10.1007/978-3-319-47099-3 9
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in GPU virtualization [1], sharing GPUs among cloud tenants is increasingly becoming norm. For example, major cloud vendors such as Amazon and Alibaba both offer GPU support for customers. However, this poses great challenges in guaranteeing strong isolation between different tenants sharing GPU devices. As will be discussed in this paper, common well-studied security vulnerabilities on CPUs, such as the stack and heap overflow and integer overflow, exist on GPUs too. Unfortunately, with high concurrency and lacking effective protection, GPUs are subject to greater threat. In
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