Daniel Klischies (Ruhr University Bochum), Philipp Mackensen (Ruhr University Bochum), Veelasha Moonsamy (Ruhr University Bochum)

Vulnerabilities in Android smartphone chipsets have severe consequences, as recent real-world attacks have demonstrated that adversaries can leverage vulnerabilities to execute arbitrary code or exfiltrate confidential information. Despite the far-reaching impact of such attacks, the lifecycle of chipset vulnerabilities has yet to be investigated, with existing papers primarily investigating vulnerabilities in the Android operating system. This paper provides a comprehensive and empirical study of the current state of smartphone chipset vulnerability management within the Android ecosystem. For the first time, we create a unified knowledge base of 3,676 chipset vulnerabilities affecting 437 chipset models from all four major chipset manufacturers, combined with 6,866 smartphone models. Our analysis revealed that the same vulnerabilities are often included in multiple generations of chipsets, providing novel empirical evidence that vulnerabilities are inherited through multiple chipset generations. Furthermore, we demonstrate that the commonly accepted 90-day responsible vulnerability disclosure period is seldom adhered to. We find that a single vulnerability often affects hundreds to thousands of different smartphone models, for which update availability is, as we show, often unclear or heavily delayed. Leveraging the new insights gained from our empirical analysis, we recommend several changes that chipset manufacturers can implement to improve the security posture of their products. At the same time, our knowledge base enables academic researchers to conduct more representative evaluations of smartphone chipsets, accurately assess the impact of vulnerabilities they discover, and identify avenues for future research.

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PhantomLiDAR: Cross-modality Signal Injection Attacks against LiDAR

Zizhi Jin (Zhejiang University), Qinhong Jiang (Zhejiang University), Xuancun Lu (Zhejiang University), Chen Yan (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

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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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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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