Annika Wilde (Ruhr University Bochum), Tim Niklas Gruel (Ruhr University Bochum), Claudio Soriente (NEC Laboratories Europe), Ghassan Karame (Ruhr University Bochum)

An increasing number of distributed platforms combine Trusted Execution Environments (TEEs) with blockchains. Indeed, many hail the combination of TEEs and blockchains a good “marriage”: TEEs bring confidential computing to the blockchain while the consensus layer could help defend TEEs from forking attacks.

In this paper, we systemize how current blockchain solutions integrate TEEs and to what extent they are secure against forking attacks. To do so, we thoroughly analyze 29 proposals for TEE-based blockchains, ranging from academic proposals to production-ready platforms. We uncover a lack of consensus in the community on how to combine TEEs and blockchains. In particular, we identify four broad means to interconnect TEEs with consensus, analyze their limitations, and discuss possible remedies. Our analysis also reveals previously undocumented forking attacks on three production-ready TEE-based blockchains: Ten, Phala, and the Secret Network. We leverage our analysis to propose effective countermeasures against those vulnerabilities; we responsibly disclosed our findings to the developers of each affected platform.

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LLMPirate: LLMs for Black-box Hardware IP Piracy

Vasudev Gohil (Texas A&M University), Matthew DeLorenzo (Texas A&M University), Veera Vishwa Achuta Sai Venkat Nallam (Texas A&M University), Joey See (Texas A&M University), Jeyavijayan Rajendran (Texas A&M University)

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LeoCommon – A Ground Station Observatory Network for LEO...

Eric Jedermann, Martin Böh (University of Kaiserslautern), Martin Strohmeier (armasuisse Science & Technology), Vincent Lenders (Cyber-Defence Campus, armasuisse Science & Technology), Jens Schmitt (University of Kaiserslautern)

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DUMPLING: Fine-grained Differential JavaScript Engine Fuzzing

Liam Wachter (EPFL), Julian Gremminger (EPFL), Christian Wressnegger (Karlsruhe Institute of Technology (KIT)), Mathias Payer (EPFL), Flavio Toffalini (EPFL)

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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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