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Combined software and hardware fault injection vulnerability detection

Abstract

Fault injection is a well-known method to test the robustness and security vulnerabilities of software. Software-based and hardware-based approaches have been used to detect fault injection vulnerabilities. Software-based approaches typically rely upon simulations that can provide broad and rapid coverage, but may not correlate with genuine hardware vulnerabilities. Hardware-based experiments are indisputable in their results, but rely upon expensive expert knowledge and manual testing yielding ad hoc and extremely limited results. Further, there is very limited connection between software-based simulation results and hardware-based experiments. This work bridges software-based and hardware-based fault injection vulnerability detection by contrasting results of both approaches. This demonstrates that: not all software-based vulnerabilities can be reproduced in hardware; prior conjectures on the fault model for electromagnetic pulse attacks may not be accurate; and that there is a co-relation between software-based and hardware-based approaches. Further, combining both approaches can yield a vastly more accurate and efficient approach to detecting genuine fault injection vulnerabilities.

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Notes

  1. 1.

    ArmL is a translation tool that translates from ARM-v7 binaries to RML models.

  2. 2.

    The SimFI tool is a tool that simulates a wide variety of fault injection attacks on binaries. The tool takes a binary as an input (regardless of the binary’s architecture). Based on the chosen fault model, a mutant binary is generated, representing the simulation of the chosen fault injection attack.

  3. 3.

    Each clock cycle is approximately 40 ns.

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Correspondence to Thomas Given-Wilson.

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Given-Wilson, T., Jafri, N. & Legay, A. Combined software and hardware fault injection vulnerability detection. Innovations Syst Softw Eng 16, 101–120 (2020). https://doi.org/10.1007/s11334-020-00364-5

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Keywords

  • Fault injection
  • Vulnerability
  • Statistical model checking
  • Formal methods
  • EMP