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Documents authored by Hassanshahi, Behnaz

Document
Artifact
DOI: 10.4230/DARTS.8.2.7
Automatic Root Cause Quantification for Missing Edges in JavaScript Call Graphs (Artifact)

Authors: Madhurima Chakraborty, Renzo Olivares, Manu Sridharan, and Behnaz Hassanshahi

Published in: DARTS, Volume 8, Issue 2, Special Issue of the 36th European Conference on Object-Oriented Programming (ECOOP 2022)

Abstract
Building sound and precise static call graphs for real-world JavaScript applications poses an enormous challenge, due to many hard-to-analyze language features. Further, the relative importance of these features may vary depending on the call graph algorithm being used and the class of applications being analyzed. In this paper, we present a technique to automatically quantify the relative importance of different root causes of call graph unsoundness for a set of target applications. The technique works by identifying the dynamic function data flows relevant to each call edge missed by the static analysis, correctly handling cases with multiple root causes and inter-dependent calls. We apply our approach to perform a detailed study of the recall of a state-of-the-art call graph construction technique on a set of framework-based web applications. The study yielded a number of useful insights. We found that while dynamic property accesses were the most common root cause of missed edges across the benchmarks, other root causes varied in importance depending on the benchmark, potentially useful information for an analysis designer. Further, with our approach, we could quickly identify and fix a recall issue in the call graph builder we studied, and also quickly assess whether a recent analysis technique for Node.js-based applications would be helpful for browser-based code. All of our code and data is publicly available, and many components of our technique can be re-used to facilitate future studies.
Cite as

Madhurima Chakraborty, Renzo Olivares, Manu Sridharan, and Behnaz Hassanshahi. Automatic Root Cause Quantification for Missing Edges in JavaScript Call Graphs (Artifact). In Special Issue of the 36th European Conference on Object-Oriented Programming (ECOOP 2022). Dagstuhl Artifacts Series (DARTS), Volume 8, Issue 2, pp. 7:1-7:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{chakraborty_et_al:DARTS.8.2.7,
  author =	{Chakraborty, Madhurima and Olivares, Renzo and Sridharan, Manu and Hassanshahi, Behnaz},
  title =	{{Automatic Root Cause Quantification for Missing Edges in JavaScript Call Graphs (Artifact)}},
  pages =	{7:1--7:5},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2022},
  volume =	{8},
  number =	{2},
  editor =	{Chakraborty, Madhurima and Olivares, Renzo and Sridharan, Manu and Hassanshahi, Behnaz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.8.2.7},
  URN =		{urn:nbn:de:0030-drops-162052},
  doi =		{10.4230/DARTS.8.2.7},
  annote =	{Keywords: JavaScript, call graph construction, static program analysis}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2022.3
Automatic Root Cause Quantification for Missing Edges in JavaScript Call Graphs

Authors: Madhurima Chakraborty, Renzo Olivares, Manu Sridharan, and Behnaz Hassanshahi

Published in: LIPIcs, Volume 222, 36th European Conference on Object-Oriented Programming (ECOOP 2022)

Abstract
Building sound and precise static call graphs for real-world JavaScript applications poses an enormous challenge, due to many hard-to-analyze language features. Further, the relative importance of these features may vary depending on the call graph algorithm being used and the class of applications being analyzed. In this paper, we present a technique to automatically quantify the relative importance of different root causes of call graph unsoundness for a set of target applications. The technique works by identifying the dynamic function data flows relevant to each call edge missed by the static analysis, correctly handling cases with multiple root causes and inter-dependent calls. We apply our approach to perform a detailed study of the recall of a state-of-the-art call graph construction technique on a set of framework-based web applications. The study yielded a number of useful insights. We found that while dynamic property accesses were the most common root cause of missed edges across the benchmarks, other root causes varied in importance depending on the benchmark, potentially useful information for an analysis designer. Further, with our approach, we could quickly identify and fix a recall issue in the call graph builder we studied, and also quickly assess whether a recent analysis technique for Node.js-based applications would be helpful for browser-based code. All of our code and data is publicly available, and many components of our technique can be re-used to facilitate future studies.
Cite as

Madhurima Chakraborty, Renzo Olivares, Manu Sridharan, and Behnaz Hassanshahi. Automatic Root Cause Quantification for Missing Edges in JavaScript Call Graphs. In 36th European Conference on Object-Oriented Programming (ECOOP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 222, pp. 3:1-3:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{chakraborty_et_al:LIPIcs.ECOOP.2022.3,
  author =	{Chakraborty, Madhurima and Olivares, Renzo and Sridharan, Manu and Hassanshahi, Behnaz},
  title =	{{Automatic Root Cause Quantification for Missing Edges in JavaScript Call Graphs}},
  booktitle =	{36th European Conference on Object-Oriented Programming (ECOOP 2022)},
  pages =	{3:1--3:28},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-225-9},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{222},
  editor =	{Ali, Karim and Vitek, Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2022.3},
  URN =		{urn:nbn:de:0030-drops-162314},
  doi =		{10.4230/LIPIcs.ECOOP.2022.3},
  annote =	{Keywords: JavaScript, call graph construction, static program analysis}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2022.29
Experience: Model-Based, Feedback-Driven, Greybox Web Fuzzing with BackREST

Authors: François Gauthier, Behnaz Hassanshahi, Benjamin Selwyn-Smith, Trong Nhan Mai, Max Schlüter, and Micah Williams

Published in: LIPIcs, Volume 222, 36th European Conference on Object-Oriented Programming (ECOOP 2022)

Abstract
Following the advent of the American Fuzzy Lop (AFL), fuzzing had a surge in popularity, and modern day fuzzers range from simple blackbox random input generators to complex whitebox concolic frameworks that are capable of deep program introspection. Web application fuzzers, however, did not benefit from the tremendous advancements in fuzzing for binary programs and remain largely blackbox in nature. In this experience paper, we show how techniques like state-aware crawling, type inference, coverage and taint analysis can be integrated with a black-box fuzzer to find more critical vulnerabilities, faster (speedups between 7.4× and 25.9×). Comparing BackREST against three other web fuzzers on five large (>500 KLOC) Node.js applications shows how it consistently achieves comparable coverage while reporting more vulnerabilities than state-of-the-art. Finally, using BackREST, we uncovered eight 0-days, out of which six were not reported by any other fuzzer. All the 0-days have been disclosed and most are now public, including two in the highly popular Sequelize and Mongodb libraries.
Cite as

François Gauthier, Behnaz Hassanshahi, Benjamin Selwyn-Smith, Trong Nhan Mai, Max Schlüter, and Micah Williams. Experience: Model-Based, Feedback-Driven, Greybox Web Fuzzing with BackREST. In 36th European Conference on Object-Oriented Programming (ECOOP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 222, pp. 29:1-29:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{gauthier_et_al:LIPIcs.ECOOP.2022.29,
  author =	{Gauthier, Fran\c{c}ois and Hassanshahi, Behnaz and Selwyn-Smith, Benjamin and Mai, Trong Nhan and Schl\"{u}ter, Max and Williams, Micah},
  title =	{{Experience: Model-Based, Feedback-Driven, Greybox Web Fuzzing with BackREST}},
  booktitle =	{36th European Conference on Object-Oriented Programming (ECOOP 2022)},
  pages =	{29:1--29:30},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-225-9},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{222},
  editor =	{Ali, Karim and Vitek, Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2022.29},
  URN =		{urn:nbn:de:0030-drops-162573},
  doi =		{10.4230/LIPIcs.ECOOP.2022.29},
  annote =	{Keywords: Taint analysis, fuzzing, crawler, Node.js}
}
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