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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Ran Elgedawy (The University of Tennessee, Knoxville), John Sadik (The University of Tennessee, Knoxville), Anuj Gautam (The University of Tennessee, Knoxville), Trinity Bissahoyo (The University of Tennessee, Knoxville), Christopher Childress (The University of Tennessee, Knoxville), Jacob Leonard (The University of Tennessee, Knoxville), Clay Shubert (The University of Tennessee, Knoxville), Scott Ruoti (The University of Tennessee,…

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LightAntenna: Characterizing the Limits of Fluorescent Lamp-Induced Electromagnetic Interference

Fengchen Yang (Zhejiang University), Wenze Cui (Zhejiang University), Xinfeng Li (Zhejiang University), Chen Yan (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

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ASGARD: Protecting On-Device Deep Neural Networks with Virtualization-Based Trusted...

Myungsuk Moon (Yonsei University), Minhee Kim (Yonsei University), Joonkyo Jung (Yonsei University), Dokyung Song (Yonsei University)

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RACONTEUR: A Knowledgeable, Insightful, and Portable LLM-Powered Shell Command...

Jiangyi Deng (Zhejiang University), Xinfeng Li (Zhejiang University), Yanjiao Chen (Zhejiang University), Yijie Bai (Zhejiang University), Haiqin Weng (Ant Group), Yan Liu (Ant Group), Tao Wei (Ant Group), Wenyuan Xu (Zhejiang University)

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