Hardening binaries against more memory errors

Memory errors, such as buffer overflows and use-after-free, remain the root cause of many security vulnerabilities in modern software. The use of closed source software further exacerbates the problem, as source-based memory error mitigation cannot be applied. While many memory error detection tools exist, most are based on a single error detection methodology with resulting known limitations, such as incomplete memory error detection (redzones) or false error detections (low-fat pointers). In this paper we introduce RedFat, a memory error hardening tool for stripped binaries that is fast, practical and scalable. The core idea behind RedFat is to combine complementary error detection methodologies-redzones and low-fat pointers-in order to detect more memory errors that can be detected by each individual methodology alone. However, complementary error detection also inherits the limitations of each approach, such as false error detections from low-fat pointers. To mitigate this, we introduce a profile-based analysis that automatically determines the strongest memory error protection possible without negative side effects.

We implement RedFat on top of a scalable binary rewriting framework, and demonstrate low overheads compared to the current state-of-the-art. We show RedFat to be language agnostic on C/C++/Fortran binaries with minimal requirements, and works with stripped binaries for both position independent/dependent code. We also show that the RedFat instrumentation can scale to very large/complex binaries, such as Google Chrome.