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Measuring the effectiveness of output filtering against SQL injection attacks

Published:28 March 2014Publication History

ABSTRACT

SQL injection (SQLi) attacks present severe risks to applications; they may result in the unintended exposure, modification, corruption, or deletion of information. An error in a single line of code can introduce a vulnerability to an application, compounding the risk. There are a variety of strategies for detecting and mitigating SQLi, including but not limited to output filtering. Output filtering protects a system and its information by validating the records that are returned from the database management system. In this paper, we evaluate the effectiveness of output filtering, which has not yet been examined in the literature. We employ output filtering to protect custom Web application known to be vulnerable to SQLi attack. An experiment was performed to determine if output filtering was able to defend an application against SQLi attacks, as well as measure the potential performance impact. Results demonstrate that output filtering has the potential to defend against SQLi attacks and has a limited impact on an application's response time.

References

  1. OWASP top 10 2004: The ten most critical web application security vulnerabilities. OWASP Foundation, 1(5):8, April 2004.Google ScholarGoogle Scholar
  2. Pangolin v3.2.4 user guide, Jan 2011.Google ScholarGoogle Scholar
  3. Havij advanced SQL injection, 2012.Google ScholarGoogle Scholar
  4. Common attack pattern enumeration and classification (CAPEC)-66: SQL injection, version 2.1, June 2013.Google ScholarGoogle Scholar
  5. Common attack pattern enumeration and classification (CAPEC)-7: Blind SQL injection, version 2.1, June 2013.Google ScholarGoogle Scholar
  6. Common weakness enumeration (CWE)-89: Improper neutralization of special elements used in an SQL command ('SQL injection'), version 2.5, July 2013.Google ScholarGoogle Scholar
  7. R. Barnett. WASC threat classification: Sql injection. Technical report, Web Application Security Consortium, June 2010.Google ScholarGoogle Scholar
  8. E. Bertino, A. Kamra, and J. P. Early. Profiling database application to detect SQL injection attacks. In Performance, Computing, and Communications Conference, 2007. IPCCC 2007. IEEE International, pages 449--458, 2007.Google ScholarGoogle Scholar
  9. S. W. Boyd and A. D. Keromytis. SQLrand: Preventing SQL injection attacks. In M. Jakobsson, M. Yung, and J. Zhou, editors, Applied Cryptography and Network Security, volume 3089 of Lecture Notes in Computer Science, pages 292--302. Springer Berlin Heidelberg, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  10. S. M. Christey. 2010 CWE/SANS top 25 most dangerous software errors. 2010.Google ScholarGoogle Scholar
  11. S. M. Christey. 2011 CWE/SANS top 25 most dangerous software errors. 2011.Google ScholarGoogle Scholar
  12. C. Cochin. SQLiX, 2006.Google ScholarGoogle Scholar
  13. B. Damele and S. M. SQLmap user's guide, 2013.Google ScholarGoogle Scholar
  14. X. Fu, X. Lu, B. Peltsverger, S. Chen, K. Qian, and L. Tao. A static analysis framework for detecting SQL injection vulnerabilities. In Computer Software and Applications Conference, 2007. COMPSAC 2007. 31st Annual International, volume 1, pages 87--96, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. W. G. J. Halfond and A. Orso. AMNESIA: analysis and monitoring for neutralizing SQL-injection attacks. In Proceedings of the 20th IEEE/ACM international Conference on Automated software engineering, ASE '05, pages 174--183, New York, NY, USA, 2005. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. W. G. J. Halfond, J. Viegas, and A. Orso. A classification of SQL-injection attacks and countermeasures. In Proceedings of the IEEE International Symposium on Secure Software Engineering, Arlington, VA, USA, pages 13--15, 2006.Google ScholarGoogle Scholar
  17. H. Shahriar and M. Zulkernine. Information-theoretic detection of sql injection attacks. In High-Assurance Systems Engineering (HASE), 2012 IEEE 14th International Symposium on, pages 40--47. IEEE, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. A. Stock, J. Williams, and D. Wichers. OWASP top 10 2007: The ten most critical web application security vulnerabilities. OWASP Foundation, 1(5):8, April 2007.Google ScholarGoogle Scholar
  19. S.-T. Sun, T. H. Wei, S. Liu, and S. Lau. Classification of SQL injection attacks. University of British Columbia, Term Project, 2007.Google ScholarGoogle Scholar
  20. S. Thomas, L. Williams, and T. Xie. On automated prepared statement generation to remove SQL injection vulnerabilities. Information and Software Technology, 51(3):589--598, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. K. Wei, M. Muthuprasanna, and S. Kothari. Preventing SQL injection attacks in stored procedures. In Software Engineering Conference, 2006. Australian, pages 8 pp.--, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. D. Wichers, J. Manico, and M. Seil. SQL injection prevention cheat sheet, December 2012.Google ScholarGoogle Scholar
  23. J. Williams and D. Wichers. OWASP top 10 2010: The ten most critical web application security vulnerabilities. OWASP Foundation, 1(5):8, April 2010.Google ScholarGoogle Scholar
  24. J. Williams and D. Wichers. OWASP top 10 2013: The ten most critical web application security risks. OWASP Foundation, 1(5):8, April 2013.Google ScholarGoogle Scholar

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              cover image ACM Other conferences
              ACM SE '14: Proceedings of the 2014 ACM Southeast Regional Conference
              March 2014
              265 pages
              ISBN:9781450329231
              DOI:10.1145/2638404
              • Conference Chair:
              • Ken Hoganson,
              • Program Chair:
              • Selena He

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              Publication History

              • Published: 28 March 2014

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