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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Reinforcement Unlearning

Dayong Ye (University of Technology Sydney), Tianqing Zhu (City University of Macau), Congcong Zhu (City University of Macau), Derui Wang (CSIRO’s Data61), Kun Gao (University of Technology Sydney), Zewei Shi (CSIRO’s Data61), Sheng Shen (Torrens University Australia), Wanlei Zhou (City University of Macau), Minhui Xue (CSIRO's Data61)

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The Discriminative Power of Cross-layer RTTs in Fingerprinting Proxy...

Diwen Xue (University of Michigan), Robert Stanley (University of Michigan), Piyush Kumar (University of Michigan), Roya Ensafi (University of Michigan)

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Victim-Centred Abuse Investigations and Defenses for Social Media Platforms

Zaid Hakami (Florida International University and Jazan University), Ashfaq Ali Shafin (Florida International University), Peter J. Clarke (Florida International University), Niki Pissinou (Florida International University), and Bogdan Carbunar (Florida International University)

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