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Deemon: Detecting CSRF with Dynamic Analysis and Property Graphs

Published: 30 October 2017 Publication History
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  • Abstract

    Cross-Site Request Forgery (CSRF) vulnerabilities are a severe class of web vulnerabilities that have received only marginal attention from the research and security testing communities. While much effort has been spent on countermeasures and detection of XSS and SQLi, to date, the detection of CSRF vulnerabilities is still performed predominantly manually.
    In this paper, we present Deemon, to the best of our knowledge the first automated security testing framework to discover CSRF vulnerabilities. Our approach is based on a new modeling paradigm which captures multiple aspects of web applications, including execution traces, data flows, and architecture tiers in a unified, comprehensive property graph. We present the paradigm and show how a concrete model can be built automatically using dynamic traces.Then, using graph traversals, we mine for potentially vulnerable operations. Using the information captured in the model, our approach then automatically creates and conducts security tests, to practically validate the found CSRF issues. We evaluate the effectiveness of Deemon with 10 popular open source web applications. Our experiments uncovered 14 previously unknown CSRF vulnerabilities that can be exploited, for instance, to take over user accounts or entire websites.

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    1. Deemon: Detecting CSRF with Dynamic Analysis and Property Graphs

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      cover image ACM Conferences
      CCS '17: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
      October 2017
      2682 pages
      ISBN:9781450349468
      DOI:10.1145/3133956
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 30 October 2017

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      Author Tags

      1. cross-site request forgery
      2. csrf
      3. dynamic analysis
      4. property graphs
      5. vulnerability analysis
      6. web security

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      • Research-article

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      • Bundesministerium für Bildung und Forschung
      • Bundesministerium für Bildung und Forschung - BMBF

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      CCS '17
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      CCS '17 Paper Acceptance Rate 151 of 836 submissions, 18%;
      Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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      CCS '24
      ACM SIGSAC Conference on Computer and Communications Security
      October 14 - 18, 2024
      Salt Lake City , UT , USA

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      • (2024)Efficient Static Vulnerability Analysis for JavaScript with Multiversion Dependency GraphsProceedings of the ACM on Programming Languages10.1145/36563948:PLDI(417-441)Online publication date: 20-Jun-2024
      • (2024)RecurScan: Detecting Recurring Vulnerabilities in PHP Web ApplicationsProceedings of the ACM on Web Conference 202410.1145/3589334.3645530(1746-1755)Online publication date: 13-May-2024
      • (2024)Detect Potentially Risky Websites using Hidden Markov Model2024 International Conference on Inventive Computation Technologies (ICICT)10.1109/ICICT60155.2024.10544876(1120-1123)Online publication date: 24-Apr-2024
      • (2023)DISOV: Discovering Second-Order Vulnerabilities Based on Web Application Property GraphIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences10.1587/transfun.2022EAP1045E106.A:2(133-145)Online publication date: 1-Feb-2023
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      • (2023)Study of JavaScript Static Analysis Tools for Vulnerability Detection in Node.js PackagesIEEE Transactions on Reliability10.1109/TR.2023.328630172:4(1324-1339)Online publication date: Dec-2023
      • (2023)RuleKeeper: GDPR-Aware Personal Data Compliance for Web Frameworks2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179395(2817-2834)Online publication date: May-2023
      • (2023)Scaling JavaScript Abstract Interpretation to Detect and Exploit Node.js Taint-style Vulnerability2023 IEEE Symposium on Security and Privacy (SP)10.1109/SP46215.2023.10179352(1059-1076)Online publication date: May-2023
      • (2023)Cross-Site Request Forgery as an Example of Machine Learning for Web Vulnerability Detection2023 3rd International Conference on Smart Data Intelligence (ICSMDI)10.1109/ICSMDI57622.2023.00080(422-426)Online publication date: Mar-2023
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