Yuri Gbur (Technische Universität Berlin), Florian Tschorsch (Technische Universität Berlin)

The QUIC protocol is gaining more and more traction through its recent standardization and the rising interest by various big tech companies, developing new implementations. QUIC promises to make security and privacy a first-class citizen; yet, challenging these claims is of utmost importance. To this end, this paper provides an initial analysis of client-side request forgery attacks that directly emerge from the QUIC protocol design and not from common vulnerabilities. In particular, we investigate three request forgery attack modalities with respect to their capabilities to be used for protocol impersonation and traffic amplification. We analyze the controllable attack space of the respective protocol messages and demonstrate that one of the attack modalities can indeed be utilized to impersonate other UDP-based protocols, e.g., DNS requests. Furthermore, we identify traffic amplification vectors. Although the QUIC protocol specification states anti-amplification limits, our evaluation of 13 QUIC server implementations shows that in some cases these mitigations are missing or insufficiently implemented. Lastly, we propose mitigation approaches for protocol impersonation and discuss ambiguities in the specification.

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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

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WIP: AMICA: Attention-based Multi-Identifier model for asynchronous intrusion detection...

Natasha Alkhatib (Télécom Paris), Lina Achaji (INRIA), Maria Mushtaq (Télécom Paris), Hadi Ghauch (Télécom Paris), Jean-Luc Danger (Télécom Paris)

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PPA: Preference Profiling Attack Against Federated Learning

Chunyi Zhou (Nanjing University of Science and Technology), Yansong Gao (Nanjing University of Science and Technology), Anmin Fu (Nanjing University of Science and Technology), Kai Chen (Chinese Academy of Science), Zhiyang Dai (Nanjing University of Science and Technology), Zhi Zhang (CSIRO's Data61), Minhui Xue (CSIRO's Data61), Yuqing Zhang (University of Chinese Academy of Science)

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AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot...

Hossein Fereidooni (Technical University of Darmstadt), Jan Koenig (University of Wuerzburg), Phillip Rieger (Technical University of Darmstadt), Marco Chilese (Technical University of Darmstadt), Bora Goekbakan (KOBIL, Germany), Moritz Finke (University of Wuerzburg), Alexandra Dmitrienko (University of Wuerzburg), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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