ARM has become the most competitive processor architecture. Many platforms or tools are developed to execute or analyze ARM instructions, including various commercial CPUs, emulators, and binary analysis tools. However, they have deviations when processing the same ARM instructions, and little attention has been paid to systematically analyze such semantic deviations, not to mention the security implications of such deviations. In this paper, we conduct an empirical study on the ARM Instruction Semantic Deviation (ISDev) issue. First, we classify this issue into several categories and analyze the security implications behind them. Then, we further demonstrate several novel attacks which utilize the ISDev issue, including stealthy targeted attacks and targeted defense evasion. Such attacks could exploit the semantic deviations to generate malware that is specific to certain platforms or able to detect and bypass certain detection solutions. We have developed a framework iDEV to systematically explore the ISDev issue in existing ARM instructions processing tools and platforms via differential testing. We have evaluated iDEV on four hardware devices, the QEMU emulator, and five disassemblers which could process the ARMv7-A instruction set. The evaluation results show that, over six million instructions could cause dynamic executors (i.e., CPUs and QEMU) to present different runtime behaviors, and over eight million instructions could cause static disassemblers yielding different decoding results, and over one million instructions cause inconsistency between dynamic executors and static disassemblers. After analyzing the root causes of each type of deviation, we point out they are mostly due to ARM unpredictable instructions and program defects.
CITATION STYLE
Qin, S., Zhang, C., Chen, K., & Li, Z. (2021). IDEV: Exploring and exploiting semantic deviations in ARM instruction processing. In ISSTA 2021 - Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 580–592). Association for Computing Machinery, Inc. https://doi.org/10.1145/3460319.3464842
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