Evan Johnson (University of California San Diego), David Thien (University of California San Diego), Yousef Alhessi (University of California San Diego), Shravan Narayan (University Of California San Diego), Fraser Brown (Stanford University), Sorin Lerner (University of California San Diego), Tyler McMullen (Fastly Labs), Stefan Savage (University of California San Diego), Deian Stefan (University of California San Diego)

WebAssembly (Wasm) is a platform-independent bytecode that offers both good performance and runtime isolation. To implement isolation, the compiler inserts safety checks when it compiles Wasm to native machine code. While this approach is cheap, it also requires trust in the compiler's correctness---trust that the compiler has inserted each necessary check, correctly formed, in each proper place. Unfortunately, subtle bugs in the Wasm compiler can break---and emph{have broken}---isolation guarantees. To address this problem, we propose verifying memory isolation of Wasm binaries post-compilation. We implement this approach in VeriWasm, a static offline verifier for native x86-64 binaries compiled from Wasm; we prove the verifier's soundness, and find that it can detect bugs with no false positives. Finally, we describe our deployment of VeriWasm at Fastly.

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XDA: Accurate, Robust Disassembly with Transfer Learning

Kexin Pei (Columbia University), Jonas Guan (University of Toronto), David Williams-King (Columbia University), Junfeng Yang (Columbia University), Suman Jana (Columbia University)

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Flexsealing BGP Against Route Leaks: Peerlock Active Measurement and...

Tyler McDaniel (University of Tennessee, Knoxville), Jared M. Smith (University of Tennessee, Knoxville), Max Schuchard (University of Tennessee, Knoxville)

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FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data

Junjie Liang (The Pennsylvania State University), Wenbo Guo (The Pennsylvania State University), Tongbo Luo (Robinhood), Vasant Honavar (The Pennsylvania State University), Gang Wang (University of Illinois at Urbana-Champaign), Xinyu Xing (The Pennsylvania State University)

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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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