RevARM: A platform-Agnostic arm binary rewriter for security applications

Taegyu Kim, Chung Hwan Kim, Hongjun Choi, Yonghwi Kwon, Brendan Saltaformaggio, Xiangyu Zhang, Dongyan Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

26 Scopus citations


ARM is the leading processor architecture in the emerging mobile and embedded market. Unfortunately, there has been a myriad of security issues on both mobile and embedded systems. While many countermeasures of such security issues have been proposed in recent years, a majority of applications still cannot be patched or protected due to run-Time and space overhead constraints and the unavailability of source code. More importantly, the rapidly evolving mobile and embedded market makes any platform-specific solution ineffective. In this paper, we propose RevARM, a binary rewriting technique capable of instrumenting ARM-based binaries without limitation on the target platform. Unlike many previous binary instrumentation tools that are designed to instrument binaries based on x86, RevARM must resolve a number of new, ARM-specific binary rewriting challenges. Moreover, RevARM is able to handle stripped binaries, requires no symbolic/semantic information, and supports Mach-O binaries, overcoming the limitations of existing approaches. Finally, we demonstrate the capabilities of RevARM in solving real-world security challenges. Our evaluation results across a variety of platforms, including popular mobile and embedded systems, show that RevARM is highly effective in instrumenting ARM binaries with an average of 3.2% run-Time and 1.3% space overhead.

Original languageEnglish (US)
Title of host publicationProceedings - 33rd Annual Computer Security Applications Conference, ACSAC 2017
PublisherAssociation for Computing Machinery
Number of pages13
ISBN (Electronic)9781450353458
StatePublished - Dec 4 2017
Event33rd Annual Computer Security Applications Conference, ACSAC 2017 - Orlando, United States
Duration: Dec 4 2017Dec 8 2017

Publication series

NameACM International Conference Proceeding Series
VolumePart F132521


Other33rd Annual Computer Security Applications Conference, ACSAC 2017
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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