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2020-03-27
Huang, Shiyou, Guo, Jianmei, Li, Sanhong, Li, Xiang, Qi, Yumin, Chow, Kingsum, Huang, Jeff.  2019.  SafeCheck: Safety Enhancement of Java Unsafe API. 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE). :889–899.

Java is a safe programming language by providing bytecode verification and enforcing memory protection. For instance, programmers cannot directly access the memory but have to use object references. Yet, the Java runtime provides an Unsafe API as a backdoor for the developers to access the low- level system code. Whereas the Unsafe API is designed to be used by the Java core library, a growing community of third-party libraries use it to achieve high performance. The Unsafe API is powerful, but dangerous, which leads to data corruption, resource leaks and difficult-to-diagnose JVM crash if used improperly. In this work, we study the Unsafe crash patterns and propose a memory checker to enforce memory safety, thus avoiding the JVM crash caused by the misuse of the Unsafe API at the bytecode level. We evaluate our technique on real crash cases from the openJDK bug system and real-world applications from AJDK. Our tool reduces the efforts from several days to a few minutes for the developers to diagnose the Unsafe related crashes. We also evaluate the runtime overhead of our tool on projects using intensive Unsafe operations, and the result shows that our tool causes a negligible perturbation to the execution of the applications.

2019-12-17
Huang, Jeff.  2018.  UFO: Predictive Concurrency Use-After-Free Detection. 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE). :609-619.

Use-After-Free (UAF) vulnerabilities are caused by the program operating on a dangling pointer and can be exploited to compromise critical software systems. While there have been many tools to mitigate UAF vulnerabilities, UAF remains one of the most common attack vectors. UAF is particularly di cult to detect in concurrent programs, in which a UAF may only occur with rare thread schedules. In this paper, we present a novel technique, UFO, that can precisely predict UAFs based on a single observed execution trace with a provably higher detection capability than existing techniques with no false positives. The key technical advancement of UFO is an extended maximal thread causality model that captures the largest possible set of feasible traces that can be inferred from a given multithreaded execution trace. By formulating UAF detection as a constraint solving problem atop this model, we can explore a much larger thread scheduling space than classical happens-before based techniques. We have evaluated UFO on several real-world large complex C/C++ programs including Chromium and FireFox. UFO scales to real-world systems with hundreds of millions of events in their execution and has detected a large number of real concurrency UAFs.