Rongzhen Cui (University of Toronto), Lianying Zhao (Carleton University), David Lie (University of Toronto)

There has been interest in mechanisms that enable the secure use of legacy code to implement trusted code in a Trusted Execution Environment (TEE), such as Intel SGX. However, because legacy code generally assumes the presence of an operating system, this naturally raises the spectre of Iago attacks on the legacy code. We observe that not all legacy code is vulnerable to Iago attacks and that legacy code must use return values from system calls in an unsafe way to have Iago vulnerabilities.

Based on this observation, we develop Emilia, which automatically detects Iago vulnerabilities in legacy applications by fuzzing applications using system call return values. We use Emilia to discover 51 Iago vulnerabilities in 17 applications, and find that Iago vulnerabilities are widespread and common. We conduct an in-depth analysis of the vulnerabilities we found and conclude that while common, the majority (82.4%) can be mitigated with simple, stateless checks in the system call forwarding layer, while the rest are best fixed by finding and patching them in the legacy code. Finally, we study and evaluate different trade-offs in the design of Emilia.

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POSEIDON: Privacy-Preserving Federated Neural Network Learning

Sinem Sav (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), David Froelicher (EPFL), Jean-Philippe Bossuat (EPFL), Joao Sa Sousa (EPFL), Jean-Pierre Hubaux (EPFL)

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RandRunner: Distributed Randomness from Trapdoor VDFs with Strong Uniqueness

Philipp Schindler (SBA Research), Aljosha Judmayer (SBA Research), Markus Hittmeir (SBA Research), Nicholas Stifter (SBA Research, TU Wien), Edgar Weippl (Universität Wien)

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ROV++: Improved Deployable Defense against BGP Hijacking

Reynaldo Morillo (University of Connecticut), Justin Furuness (University of Connecticut), Cameron Morris (University of Connecticut), James Breslin (University of Connecticut), Amir Herzberg (University of Connecticut), Bing Wang (University of Connecticut)

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