Taemin Park (University of California, Irvine), Karel Dhondt (imec-DistriNet, KU Leuven), David Gens (University of California, Irvine), Yeoul Na (University of California, Irvine), Stijn Volckaert (imec-DistriNet, KU Leuven), Michael Franz (University of California, Irvine, USA)

Data-only attacks against dynamic scripting environments have become common. Web browsers and other modern applications embed scripting engines to support interactive content. The scripting engines optimize performance via just-in-time compilation. Since applications are increasingly hardened against code-reuse attacks, adversaries are looking to achieve code execution or elevate privileges by corrupting sensitive data like the intermediate representation of optimizing JIT compilers. This has inspired numerous defenses for just-in-time compilers.

Our paper demonstrates that securing JIT compilation is not sufficient. First, we present a proof-of-concept data-only attack against a recent version of Mozilla’s SpiderMonkey JIT in which the attacker only corrupts heap objects to successfully issue a system call from within bytecode execution at run time. Previous work assumed that bytecode execution is safe by construction since interpreters only allow a narrow set of benign instructions and bytecode is always checked for validity before execution. We show that this does not prevent malicious code execution in practice. Second, we design a novel defense, dubbed NoJITsu to protect complex, real-world scripting engines from data-only attacks against interpreted code. The key idea behind our defense is to allow fine-grained memory access control by analyzing, identifying, isolating, and protecting individual memory regions focusing on their role in code generation at any point in the JavaScript engine. For this we combine automated analysis and instrumentation, compartmentalization, and Intel’s Memory-Protection Keys to secure SpiderMonkey against previous and our new attack. We implement and thoroughly test our implementation using a number of real-world scenarios as well as standard benchmarks. We show that NoJITsu successfully thwarts code-reuse as well as data-only attacks against any part of the scripting engine while offering a modest run-time overhead of only 5%.

View More Papers

BLAZE: Blazing Fast Privacy-Preserving Machine Learning

Arpita Patra (Indian Institute of Science, Bangalore), Ajith Suresh (Indian Institute of Science, Bangalore)

Read More

DISCO: Sidestepping RPKI's Deployment Barriers

Tomas Hlavacek (Fraunhofer SIT), Italo Cunha (Universidade Federal de Minas Gerais), Yossi Gilad (Hebrew University of Jerusalem), Amir Herzberg (University of Connecticut), Ethan Katz-Bassett (Columbia University), Michael Schapira (Hebrew University of Jerusalem), Haya Shulman (Fraunhofer SIT)

Read More

MassBrowser: Unblocking the Censored Web for the Masses, by...

Milad Nasr (University of Massachusetts Amherst), Hadi Zolfaghari (University of Massachusetts Amherst), Amir Houmansadr (University of Massachusetts Amherst), Amirhossein Ghafari (University of Massachusetts Amherst)

Read More

HFL: Hybrid Fuzzing on the Linux Kernel

Kyungtae Kim (Purdue University), Dae R. Jeong (KAIST), Chung Hwan Kim (NEC Labs America), Yeongjin Jang (Oregon State University), Insik Shin (KAIST), Byoungyoung Lee (Seoul National University)

Read More