HyungSeok Han (KAIST), DongHyeon Oh (KAIST), Sang Kil Cha (KAIST)

JavaScript engines are an attractive target for attackers due to their popularity and flexibility in building exploits. Current state-of-the-art fuzzers for finding JavaScript engine vulnerabilities focus mainly on generating syntactically correct test cases based on either a predefined context-free grammar or a trained probabilistic language model. Unfortunately, syntactically correct JavaScript sentences are often semantically invalid at runtime. Furthermore, statically analyzing the semantics of JavaScript code is challenging due to its dynamic nature: JavaScript code is generated at runtime, and JavaScript expressions are dynamically-typed. To address this challenge, we propose a novel test case generation algorithm that we call semantics-aware assembly, and implement it in a fuzz testing tool termed CodeAlchemist. Our tool can generate arbitrary JavaScript code snippets that are both semantically and syntactically correct, and it effectively yields test cases that can crash JavaScript engines. We found numerous vulnerabilities of the latest JavaScript engines with CodeAlchemist and reported them to the vendors.

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SANCTUARY: ARMing TrustZone with User-space Enclaves

Ferdinand Brasser (Technische Universität Darmstadt), David Gens (Technische Universität Darmstadt), Patrick Jauernig (Technische Universität Darmstadt), Ahmad-Reza Sadeghi (Technische Universität Darmstadt), Emmanuel Stapf (Technische Universität Darmstadt)

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Measurement and Analysis of Hajime, a Peer-to-peer IoT Botnet

Stephen Herwig (University of Maryland), Katura Harvey (University of Maryland, Max Planck Institute for Software Systems (MPI-SWS)), George Hughey (University of Maryland), Richard Roberts (University of Maryland, Max Planck Institute for Software Systems (MPI-SWS)), Dave Levin (University of Maryland)

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Enemy At the Gateways: Censorship-Resilient Proxy Distribution Using Game...

Milad Nasr (University of Massachusetts Amherst), Sadegh Farhang (Pennsylvania State University), Amir Houmansadr (University of Massachusetts Amherst), Jens Grossklags (Technical University of Munich)

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A Systematic Framework to Generate Invariants for Anomaly Detection...

Cheng Feng (Imperial College London & Siemens Corporate Technology), Venkata Reddy Palleti (Singapore University of Technology and Design), Aditya Mathur (Singapore University of Technology and Design), Deeph Chana (Imperial College London)

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