2022/03/12 by Nima Shiri Harzevili, Jiho Shin, Harzevili, Nima Shiri +5 · 3 citations
Computer Science · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Software Engineering (cs.SE) #Software Engineering Research #Software Reliability and Analysis Research
paper · pdf · doi:10.48550/arxiv.2203.06502
openalex publication_date 2022/03/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The application of machine learning (ML) libraries has been tremendously increased in many domains, including autonomous driving systems, medical, and critical industries. Vulnerabilities of such libraries result in irreparable consequences. However, the characteristics of software security vulnerabilities have not been well studied. In this paper, to bridge this gap, we take the first step towards characterizing and understanding the security vulnerabilities of five well-known ML libraries, including Tensorflow, PyTorch, Sickit-learn, Pandas, and Numpy. To do so, in total, we collected 596 security-related commits to exploring five major factors: 1) vulnerability types, 2) root causes, 3) symptoms, 4) fixing patterns, and 5) fixing efforts of security vulnerabilities in ML libraries. The findings of this study can assist developers in having a better understanding of software security vulnerabilities across different ML libraries and gain a better insight into their weaknesses of them. To make our finding actionable, we further developed DeepMut, an automated mutation testing tool, as a proof-of-concept application of our findings. DeepMut is designed to assess the adequacy of existing test suites of ML libraries against security-aware mutation operators extracted from the vulnerabilities studied in this work. We applied DeepMut on the Tensorflow kernel module and found more than 1k alive mutants not considered by the existing test suits. The results demonstrate the usefulness of our findings.