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Inspecting JavaScript Vulnerability Mitigation Patches with Automated Fix Generation in Mind

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12252))

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Abstract

Software security has become a primary concern for both the industry and academia in recent years. As dependency on critical services provided by software systems grows globally, a potential security threat in such systems poses higher and higher risks (e.g. economical damage, a threat to human life, criminal activity).

Finding potential security vulnerabilities at the code level automatically is a very popular approach to aid security testing. However, most of the methods based on machine learning and statistical models stop at listing potentially vulnerable code parts and leave their validation and mitigation to the developers. Automatic program repair could fill this gap by automatically generating vulnerability mitigation code patches. Nonetheless, it is still immature, especially in targeting security-relevant fixes.

In this work, we try to establish a path towards automatic vulnerability fix generation techniques in the context of JavaScript programs. We inspect 361 actual vulnerability mitigation patches collected from vulnerability databases and GitHub. We found that vulnerability mitigation patches are not short on average and in many cases affect not just program code but test code as well. These results point towards that a general automatic repair approach targeting all the different types of vulnerabilities is not feasible. The analysis of the code properties and fix patterns for different vulnerability types might help in setting up a more realistic goal in the area of automatic JavaScript vulnerability repair.

The presented work was carried out within the SETIT Project (2018-1.2.1-NKP-2018-00004). Project no. 2018-1.2.1-NKP-2018-00004 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the 2018-1.2.1-NKP funding scheme and partially supported by grant TUDFO/47138-1/2019-ITM of the Ministry for Innovation and Technology, Hungary. Furthermore, Péter Hegedűs was supported by the Bolyai János Scholarship of the Hungarian Academy of Sciences and the ÚNKP-19-4-SZTE-20 New National Excellence Program of the Ministry for Innovation and Technology.

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Notes

  1. 1.

    https://scitools.com/.

  2. 2.

    https://doi.org/10.5281/zenodo.3767909.

  3. 3.

    npm:ws:20160624.

  4. 4.

    npm:dustjs-linkedin:20160819.

  5. 5.

    npm:chromedriver:20161208.

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Author information

Authors and Affiliations

  1. MTA-SZTE Research Group on Artificial Intelligence, Szeged, Hungary

    Péter Hegedűs

Authors
  1. Péter Hegedűs
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Correspondence to Péter Hegedűs .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Hegedűs, P. (2020). Inspecting JavaScript Vulnerability Mitigation Patches with Automated Fix Generation in Mind. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12252. Springer, Cham. https://doi.org/10.1007/978-3-030-58811-3_69

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  • DOI: https://doi.org/10.1007/978-3-030-58811-3_69

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58810-6

  • Online ISBN: 978-3-030-58811-3

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