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Detecting and Defending Against Certificate Attacks with Origin-Bound CAPTCHAs

  • Adil AhmadEmail author
  • Faizan Ahmad
  • Lei Wei
  • Vinod Yegneswaran
  • Fareed Zaffar
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 255)

Abstract

Published reports have highlighted various attacks on secure Public Key Infrastructure (PKI)-based SSL/TLS protocols. A well-known example of such an attack, that exploits a flaw in the Certificate Authority (CA) model of the PKI, is the compelled Man-in-the-Middle (MITM) attack, in which governments or affiliated agencies compel a CA to issue false but verifiable certificates for popular websites. These certificates are then used to hijack secure communication for censorship and surveillance purposes. Such attacks significantly undermine the confidentiality guarantees provided by SSL and the privacy of Internet users at large.

To address this issue, we present Origin-Bound CAPTCHAs (OBCs), which are dual CAPTCHA tests that elevate the difficulty of launching such attacks and make their deployment infeasible especially in cases of mass surveillance. An OBC is linked to the public key of the server and by solving the OBC, the client can use the certificate to authenticate the server and verify the confidentially of the link. Our design is distinguished from prior efforts in that it does not require bootstrapping but does require minor changes at the server side. We discuss the security provided by an OBC from the perspective of an adversary who employs a human work force and presents the findings from a controlled user study that evaluates tradeoffs in OBC design choices. We also evaluate a software prototype of this concept that demonstrates how OBCs can be implemented and deployed efficiently with 1.2-3x overhead when compared to a traditional TLS/SSL implementation.

Keywords

Compelled-certificate attacks Man-in-the-middle attacks CAPTCHAs 

Notes

Acknowledgements

We would like to thank all the anonymous reviewers of the program committee for their valuable insights on the paper. This work was partially funded by the National Science Foundation (NSF) under Grant No. CNS-1514503. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF.

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Adil Ahmad
    • 1
    Email author
  • Faizan Ahmad
    • 2
  • Lei Wei
    • 3
  • Vinod Yegneswaran
    • 4
  • Fareed Zaffar
    • 2
  1. 1.Purdue UniversityWest LafayetteUSA
  2. 2.Lahore University of Management Sciences (LUMS)LahorePakistan
  3. 3.Apple Inc.CupertinoUSA
  4. 4.SRI InternationalMenlo ParkUSA

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