A Study of Overflow Vulnerabilities on GPUs - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2016

A Study of Overflow Vulnerabilities on GPUs

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.
Fichier principal
Vignette du fichier
432484_1_En_9_Chapter.pdf (583.71 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01648002 , version 1 (24-11-2017)

Licence

Attribution

Identifiers

Cite

Bang Di, Jianhua Sun, Hao Chen. A Study of Overflow Vulnerabilities on GPUs. 13th IFIP International Conference on Network and Parallel Computing (NPC), Oct 2016, Xi'an, China. pp.103-115, ⟨10.1007/978-3-319-47099-3_9⟩. ⟨hal-01648002⟩
142 View
1075 Download

Altmetric

Share

Gmail Facebook X LinkedIn More