Chendong Yu (Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences), Yang Xiao (Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences), Jie Lu (Institute of Computing Technology of the Chinese Academy of Sciences), Yuekang Li (University of New South Wales), Yeting Li (Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences), Lian Li (Institute of Computing Technology of the Chinese Academy of Sciences), Yifan Dong (Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences), Jian Wang (Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences), Jingyi Shi (Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences), Defang Bo (Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences), Wei Huo (Institute of Information Engineering, Chinese Academy of Sciences and School of Cyber Security, University of Chinese Academy of Sciences)

Files are a significant attack vector for security boundary violation, yet a systematic understanding of the vulnerabilities underlying these attacks is lacking. To bridge this gap, we present a comprehensive analysis of File Hijacking Vulnerabilities (FHVulns), a type of vulnerability that enables attackers to breach security boundaries through the manipulation of file content or file paths. We provide an in-depth empirical study on 268 well-documented FHVuln CVE records from January 2020 to October 2022. Our study reveals the origins and triggering mechanisms of FHVulns and highlights that existing detection techniques have overlooked the majority of FHVulns. As a result, we anticipate a significant prevalence of zero-day FHVulns in software. We developed a dynamic analysis tool, JERRY, which effectively detects FHVulns at runtime by simulating hijacking actions during program execution. We applied JERRY to 438 popular software programs from vendors including Microsoft, Google, Adobe, and Intel, and found 339 zero-day FHVulns. We reported all vulnerabilities identified by JERRY to the corresponding vendors, and as of now, 84 of them have been confirmed or fixed, with 51 CVE IDs granted and $83,400 bug bounties earned.