SPLAT: A Tool for Model-Checking and Dynamically-Enforcing Abstractions

  • Anil Madhavapeddy
  • David Scott
  • Richard Sharp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3639)

Abstract

Conventional software model-checking involves (i) creating an abstract model of a complex application; (ii) validating this model against the application; and (iii) checking safety properties against the abstract model. To non-experts, steps (i) and (ii) are often the most daunting. Firstly how does one decide which aspects of the application to include in the abstract model? Secondly, how does one determine whether the abstraction inadvertently “hides” critical bugs? Similarly, if a counter-example is found, how does one determine whether this is a genuine bug or just a modelling artifact?

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References

  1. 1.
    Leroy, X., et al.: Objective Caml, http://caml.inria.fr/
  2. 2.
    CERT Coordination Center (CERT/CC). CERT knowledgebase, http://www.cert.org/kb/
  3. 3.
    Henzinger, T.A., Jhala, R., Majumdar, R., Necula, G.C., Sutre, G., Weimer, W.: Temporal-safety proofs for systems code. In: Brinksma, E., Larsen, K.G. (eds.) CAV 2002. LNCS, vol. 2404, pp. 526–538. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. 4.
    Holzmann, G.J.: The SPIN Model Checker: Primer and Reference Manual title. Pearson Educational (2003)Google Scholar
  5. 5.
    Provos, N., Friedl, M., Honeyman, P.: Preventing privilege escalation. In: Proceedings of the 12th USENIX Security Symposium (August 2003)Google Scholar
  6. 6.
    Bill Sommerfeld. IETF Secure Shell Working Group (secsh), http://ietf.org/html.charters/secsh-charter.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Anil Madhavapeddy
    • 1
  • David Scott
    • 2
  • Richard Sharp
    • 3
  1. 1.Computer LaboratoryUniversity of Cambridge 
  2. 2.Fraser Research 
  3. 3.Intel Research Cambridge 

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