Kyle Zeng (Arizona State University), Moritz Schloegel (CISPA Helmholtz Center for Information Security), Christopher Salls (UC Santa Barbara), Adam Doupé (Arizona State University), Ruoyu Wang (Arizona State University), Yan Shoshitaishvili (Arizona State University), Tiffany Bao (Arizona State University)
Code reuse attacks are one of the most crucial cornerstones of modern memory corruption-based attacks. However, the task of stitching gadgets together remains a time-consuming and manual process. Plenty of research has been published over the past decade that aims at automating this problem, but very little has been adopted in practice. Solutions are often impractical in terms of performance or supported architectures, or they fail to generate a valid chain. A systematic analysis reveals they all use a generate-and-test approach, where they first enumerate all gadgets and then use symbolic execution or SMT solvers to reason about which gadgets to combine into a chain.Unfortunately, this approach scales exponentially to the number of available gadgets, thus limiting scalability on larger binaries.
In this work, we revisit this fundamental strategy and propose a new grouping of gadgets, which we call ROPBlock, that exhibit one crucial difference to gadgets: ROPBlocks are guaranteed to be chainable. We combine this notion of ROPBlock with a graph search algorithm and propose a gadget chaining approach that significantly improves performance compared to prior work. We successfully reduce the time complexity of setting registers to attacker-specified values from O(2n) to O(n). This yields a 2–3 orders of magnitude speed-up in practice during chain generation. At the same time, ROPBlocks allow us to model complex gadgets—such as those involving ret2csu or with conditional branches—that most other approaches fail to consider by design. And as ROPBlocks are architecture-agnostic, our approach can be applied to diverse architectures.
Our prototype, ropbot, generates complex, real-world chains invoking dup-dup-execve within 2.5s on average for all 37 binaries in our evaluation. All but one other approach fails to generate any chain for this scenario. For mmap chains, a difficult scenario that requires setting six register values, ropbot finds chains for 5x more targets than the second-best technique. To show its versatility, we evaluate ropbot on x64, MIPS, ARM, and AArch64. We added RISC-V support in less than two hours by adding twelve lines of code. Finally, we demonstrate that ropbot outperforms all existing tools on their respective datasets.