Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 474-479.doi: 10.11896/jsjkx.210600200

• Information Security • Previous Articles     Next Articles

Empirical Security Study of Native Code in Python Virtual Machines

JIANG Cheng-man1, HUA Bao-jian1, FAN Qi-liang1, ZHU Hong-jun2, XU Bo3, PAN Zhi-zhong1   

  1. 1 School of Software Engineering,University of Science and Technology of China,Hefei 230000,China
    2 Anhui Institute of Information Technology,Wuhu,Anhui 241002,China
    3 Hefei National Laboratory for Physical Sciences at the Microscale,University of Science and Technology of China,Hefei 230000,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:JIANG Cheng-man,born in 1995,master.His main research interests include network and information security.
    HUA Bao-jian,born in 1979,Ph.D,assistant professor,graduate supervisor.His main research interests include programming language theory and implementation,computer and network security,etc.
  • Supported by:
    Graduate Education Innovation Program of USTC(2020YCJC41).

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Abstract: The Python programming language and its echo-systems continue to play important roles in modern artificial intelligent systems like machine learning or deep learning,and are among one of the most popular implementation languages in modern machine learning infrastructures like TensorFlow,PyTorch,Caffe or CNTK.The security of the Python virtual machines is critical to the security of these machine learning systems.However,due to the existence of huge native code base in Python's CPython virtual machine,it's a great research challenge to study the security vulnerability patterns in Python virtual machines and the techniques to fix these vulnerabilities.This paper presents a novel vulnerability analysis framework PyGuard,which makes use of the static program analysis techniques to analyze the security of native code in Python virtual machines.This paper also introduces a prototype implementation of this framework and reports the experimental results of an empirical security study of the CPython virtual machine (version 3.9):we have found 45 new security vulnerabilities which demonstrates the effectiveness of this system.We have conducted a thorough study of the vulnerability patterns and given a taxonomy.

Key words: Native code, Program analysis, Python virtual machines, Security vulnerabilities

CLC Number: 

  • TP311
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