Yunpeng Tian (Huazhong University of Science and Technology), Feng Dong (Huazhong University of Science and Technology), Haoyi Liu (Huazhong University of Science and Technology), Meng Xu (University of Waterloo), Zhiniang Peng (Huazhong University of Science and Technology; Sangfor Technologies Inc.), Zesen Ye (Sangfor Technologies Inc.), Shenghui Li (Huazhong University of Science and Technology), Xiapu Luo (The Hong Kong Polytechnic University), Haoyu Wang (Huazhong University of Science and Technology)

Microsoft Office is a comprehensive suite of productivity tools and Object Linking & Embedding (OLE) is a specification that standardizes the linking and embedding of a diverse set of objects across different applications.OLE facilitates data interchange and streamlines user experience when dealing with composite documents (e.g., an embedded Excel sheet in a Word document). However, inherent security weaknesses within the design of OLE present risks, as the design of OLE inherently blurs the trust boundary between first-party and third-party code, which may lead to unintended library loading and parsing vulnerabilities which could be exploited by malicious actors. Addressing this issue, this paper introduces OLExplore, a novel tool designed for security assessment of Office OLE objects.With an in-depth examination of historical OLE vulnerabilities, we have identified three key categories of vulnerabilities and subjected them to dynamic analysis and verification. Our evaluation of various Windows operating system versions has led to the discovery of 26 confirmed vulnerabilities, with 17 assigned CVE numbers that all have remote code execution potential.

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Takami Sato (University of California, Irvine), Ryo Suzuki (Keio University), Yuki Hayakawa (Keio University), Kazuma Ikeda (Keio University), Ozora Sako (Keio University), Rokuto Nagata (Keio University), Ryo Yoshida (Keio University), Qi Alfred Chen (University of California, Irvine), Kentaro Yoshioka (Keio University)

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Shuwen Liu (School of Data Science, The Chinese University of Hong Kong, Shenzhen, China), George C. Polyzos (School of Data Science, The Chinese University of Hong Kong, Shenzhen, China and ExcID P.C., Athens, Greece)

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Jie Lin (University of Central Florida), David Mohaisen (University of Central Florida)

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