Hugo Kermabon-Bobinnec (Concordia University), Yosr Jarraya (Ericsson Security Research), Lingyu Wang (Concordia University), Suryadipta Majumdar (Concordia University), Makan Pourzandi (Ericsson Security Research)

Known, but unpatched vulnerabilities represent one of the most concerning threats for businesses today. The average time-to-patch of zero-day vulnerabilities remains around 100 days in recent years. The lack of means to mitigate an unpatched vulnerability may force businesses to temporarily shut down their services, which can lead to significant financial loss. Existing solutions for filtering system calls unused by a container can effectively reduce the general attack surface, but cannot prevent a specific vulnerability that shares the same system calls with the container. On the other hand, existing provenance analysis solutions can help identify a sequence of system calls behind the vulnerability, although they do not provide a direct solution for filtering such a sequence. To bridge such a research gap, we propose Phoenix, a solution for preventing exploits of unpatched vulnerabilities by accurately and efficiently filtering sequences of system calls identified through provenance analysis. To achieve this, Phoenix cleverly combines the efficiency of Seccomp filters with the accuracy of Ptrace-based deep argument inspection, and it provides the novel capability of filtering system call sequences through a dynamic Seccomp design. Our implementation and experiments show that Phoenix can effectively mitigate real-world vulnerabilities which evade existing solutions, while introducing negligible delay (less than 4%) and less overhead (e.g., 98% less CPU consumption than existing solution).

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Certificate Transparency Revisited: The Public Inspections on Third-party Monitors

Aozhuo Sun (Institute of Information Engineering, Chinese Academy of Sciences), Jingqiang Lin (School of Cyber Science and Technology, University of Science and Technology of China), Wei Wang (Institute of Information Engineering, Chinese Academy of Sciences), Zeyan Liu (The University of Kansas), Bingyu Li (School of Cyber Science and Technology, Beihang University), Shushang Wen (School of…

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CrowdGuard: Federated Backdoor Detection in Federated Learning

Phillip Rieger (Technical University of Darmstadt), Torsten Krauß (University of Würzburg), Markus Miettinen (Technical University of Darmstadt), Alexandra Dmitrienko (University of Würzburg), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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A Preliminary Study on Using Large Language Models in...

Kumar Shashwat, Francis Hahn, Xinming Ou, Dmitry Goldgof, Jay Ligatti, Larrence Hall (University of South Florida), S. Raj Rajagoppalan (Resideo), Armin Ziaie Tabari (CipherArmor)

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