A Study of Overflow Vulnerabilities on GPUs

[Nytro]

A Study of Overflow Vulnerabilities on GPUs

 

Bang Di, Jianhua Sun and Hao Chen

College of Computer Science and Electronic Engineering,

Hunan University, Changsha 410082, China

{dibang,jhsun,haochen}@hnu.edu.cn

 

Abstract.

 

GPU-accelerated computing gains rapidly-growing popular-

ity 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 manipu-

lating 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 over-

writing 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.

 

Sursa: https://www.aimlab.org/haochen/papers/npc16-overflow.pdf