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Differential Fault Analysis Automation on Assembly Code

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Automated Methods in Cryptographic Fault Analysis

Abstract

Normally, fault attack models are determined by analyzing the cipher algorithm and finding exploitable spots in non-linear and permutation layers. However, due to optimizations and different ways of realizing algorithmic operations, cipher level analysis does not capture enough details to locate the vulnerable parts of software implementations. On the other hand, manually analyzing the assembly code requires non-negligible amount of time and expertise.

In this chapter, we propose an automated approach for analyzing cipher implementations in assembly. By representing the whole assembly program as a data flow graph, we can efficiently identify the vulnerable spots. Fault propagation is analyzed in a subgraph, constructed from each vulnerable spot, allowing equations for differential fault analysis (DFA) to be automatically generated.

We have created a tool that implements our approach: DATAC—DFA Automation Tool for Assembly Code. We have successfully used DATAC for attacking PRESENT-80, being able to find implementation-specific vulnerabilities that can be exploited in order to recover the last round key with 16 faults. Our results show that DATAC is useful in finding attack spots that are not visible from the cipher structure, but can be easily exploited when dealing with real-world implementations.

This research was conducted when the author “Jakub Breier” was with Temasek Laboratories, NTU.

This research was conducted when the author “Xiaolu Hou” was with Nanyang Technological University.

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Notes

  1. 1.

    http://www.atmel.com/images/Atmel-0856-AVR-Instruction-Set-Manual.pdf.

  2. 2.

    We note that this is only a naming convention to make the analysis consistent. In special cases, where the actual order of the key words is different from their loading sequence, user has to rearrange the key words after the analysis.

  3. 3.

    https://github.com/kostaspap88/PRESENT_speed_implementation.

  4. 4.

    https://github.com/openluopworld/simon_speck_on_avr/tree/master/AVR.

  5. 5.

    https://perso.uclouvain.be/fstandae/lightweight_ciphers/source/AES.asm.

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Author information

Authors and Affiliations

  1. Underwriters Laboratories, Singapore, Singapore

    Jakub Breier

  2. Acronis, Singapore, Singapore

    Xiaolu Hou

  3. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Yang Liu

Authors
  1. Jakub Breier
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  2. Xiaolu Hou
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  3. Yang Liu
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Corresponding author

Correspondence to Xiaolu Hou .

Editor information

Editors and Affiliations

  1. Underwriters Laboratories, Singapore, Singapore

    Jakub Breier

  2. Acronis, Singapore, Singapore

    Xiaolu Hou

  3. Temasek Laboratories, Nanyang Technological University, Singapore, Singapore

    Shivam Bhasin

Appendix: Class Diagram of DATAC

Appendix: Class Diagram of DATAC

See Fig. 4.8.

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Breier, J., Hou, X., Liu, Y. (2019). Differential Fault Analysis Automation on Assembly Code. In: Breier, J., Hou, X., Bhasin, S. (eds) Automated Methods in Cryptographic Fault Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-11333-9_4

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  • DOI: https://doi.org/10.1007/978-3-030-11333-9_4

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