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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TextBugger: Generating Adversarial Text Against Real-world Applications

Jinfeng Li (Zhejiang University), Shouling Ji (Zhejiang University), Tianyu Du (Zhejiang University), Bo Li (University of California, Berkeley), Ting Wang (Lehigh University)

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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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Profit: Detecting and Quantifying Side Channels in Networked Applications

Nicolás Rosner (University of California, Santa Barbara), Ismet Burak Kadron (University of California, Santa Barbara), Lucas Bang (Harvey Mudd College), Tevfik Bultan (University of California, Santa Barbara)

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OBFUSCURO: A Commodity Obfuscation Engine on Intel SGX

Adil Ahmad (Purdue), Byunggill Joe (KAIST), Yuan Xiao (Ohio State University), Yinqian Zhang (Ohio State University), Insik Shin (KAIST), Byoungyoung Lee (Purdue/SNU)

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