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Determining an Economic Value of High Assurance for Commodity Software Security

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Security Protocols XXVIII (Security Protocols 2023)

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Abstract

Security measures that attempt to prevent breaches of commodity software have not used high assurance methods and tools. Instead, rational defenders have risked incurring losses caused by breaches because the cost of recovery from a breach multiplied by the probability of that breach was lower than the cost of prevention by high assurance, e.g., by formal methods. This practice may change soon since breach-recovery costs have increased substantially while formal methods costs have decreased dramatically over the past decade.

We introduce the notion of selective high assurance and show that it is economically justified, as producers can easily recoup its cost even in very small commodity markets, and necessary for rational defenders to decrease their breach recovery costs below a chosen limit. However, these decreases depend on defenders’ risk aversion, which is difficult to assess since risk preferences cannot be anticipated. A challenge is to determine a lower bound on the economic value of selective high assurance independent of the defenders’ risk preferences; i.e., a value that depends only on the commodity software itself and the attacks it withstands. We propose an approach to determine such a value and illustrate it for SCION, a networking software system with provable security properties.

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Notes

  1. 1.

    CAGR stands for compound annual growth rate.

  2. 2.

    Recent cost estimates range between 0.8% [2] to slightly over 1% [3] of global GDP.

  3. 3.

    SLoC stands for Source Lines of Code.

  4. 4.

    It is possible to select C\(_b\)(verification) > C\(_b\)(recovery), e.g., using an average per-breach cost, and still satisfy the required condition for some defenders.

  5. 5.

    A decade ago, the average recovery cost of a US company was already $8.9M [14].

  6. 6.

    If C\(_b\)(verification)<C\(_b\)(recovery), \(\epsilon >1/(mn)\) >C\(_b\)(verification)/C\(_b\)(recovery)\(\cdot \)(mn).

  7. 7.

    This scaling accounts for the lowest recovery_cost(breach) = $2.9M, which assumes that advanced AI methods and tools detect and recover from breaches. This is lower than the recovery cost per breach of $3.28M in mature zero-trust architectures [13].

  8. 8.

    The 2017 NotPetya malware attack, which was attributed to Russia’s military intelligence agency in the conflict with Ukraine, was found not to be an “act of war” when deployed against the Merck pharmaceutical company, causing a $1.4B liability for Merck’s insurers  [26].

  9. 9.

    This difference reflects the behavioral-economics [16] separation between increased beliefs of trustworthiness (e.g., obtained by assurance of security properties) and decreased betrayal aversion (e..g, obtained by attack-deterrence measures) [17].

  10. 10.

    The earliest high-assurance method and automated tool for analyzing penetration-resistance properties were used on C language programs of the Trusted Xenix (https://en.wikipedia.org/wiki/Xenix) kernel and system processes [29, 30].

  11. 11.

    The US vulnerability database (see https://nvd.nist.gov/general/nvd-dashboard and https://cve.mitre.org/cve/identifiers/) currently contains over 200000 CVEs.

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Acknowledgment

Mads Dam, Kevin Foltz, Rick Kuhn, Bryan Parno, and Frank Stajano provided helpful comments on earlier drafts of this paper. We gratefully acknowledge support for this project from the Werner Siemens Stiftung (WSS) Centre for Cyber Trust at ETH Zurich and CyLab at Carnegie Mellon University.

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Gligor, V., Perrig, A., Basin, D. (2023). Determining an Economic Value of High Assurance for Commodity Software Security. In: Stajano, F., Matyáš, V., Christianson, B., Anderson, J. (eds) Security Protocols XXVIII. Security Protocols 2023. Lecture Notes in Computer Science, vol 14186. Springer, Cham. https://doi.org/10.1007/978-3-031-43033-6_23

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  • DOI: https://doi.org/10.1007/978-3-031-43033-6_23

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