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.

View More Papers

Practical Hidden Voice Attacks against Speech and Speaker Recognition...

Hadi Abdullah (University of Florida), Washington Garcia (University of Florida), Christian Peeters (University of Florida), Patrick Traynor (University of Florida), Kevin R. B. Butler (University of Florida), Joseph Wilson (University of Florida)

Read More

Nearby Threats: Reversing, Analyzing, and Attacking Google’s ‘Nearby Connections’...

Daniele Antonioli (Singapore University of Technology and Design (SUTD)), Nils Ole Tippenhauer (CISPA), Kasper Rasmussen (University of Oxford)

Read More

Cybercriminal Minds: An investigative study of cryptocurrency abuses in...

Seunghyeon Lee (KAIST, S2W LAB Inc.), Changhoon Yoon (S2W LAB Inc.), Heedo Kang (KAIST), Yeonkeun Kim (KAIST), Yongdae Kim (KAIST), Dongsu Han (KAIST), Sooel Son (KAIST), Seungwon Shin (KAIST, S2W LAB Inc.)

Read More