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TAJ: Effective Taint Analysis of Web Applications
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BibTeX
@MISC{Pistoia_taj:effective,
author = {Marco Pistoia and Stephen Fink and Manu Sridharan and Omri Weisman},
title = {TAJ: Effective Taint Analysis of Web Applications},
year = {}
}
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Abstract
Taint analysis, a form of information-flow analysis, establishes whether values from untrusted methods and parameters may flow into security-sensitive operations. Taint analysis can detect many common vulnerabilities in Web applications, and so has attracted much attention from both the research community and industry. However, most static taint-analysis tools do not address critical requirements for an industrial-strength tool. Specifically, an industrial-strength tool must scale to large industrial Web applications, model essential Web-application code artifacts, and generate consumable reports for a wide range of attack vectors. We have designed and implemented a static Taint Analysis for Java (TAJ) that meets the requirements of industry-level applications. TAJ can analyze applications of virtually any size, as it employs a set of techniques designed to produce useful answers given limited time and space. TAJ addresses a wide variety of attack vectors, with techniques to handle reflective calls, flow through containers, nested taint, and issues in generating useful reports. This paper provides a description of the algorithms comprising TAJ, evaluates TAJ against production-level benchmarks, and compares it with alternative solutions.
Keyphrases
web application effective taint analysis attack vector industrial-strength tool taint analysis large industrial web application untrusted method information-flow analysis alternative solution many common vulnerability wide range useful report static taint analysis limited time critical requirement useful answer reflective call model essential web-application code artifact much attention consumable report security-sensitive operation industry-level application static taint-analysis tool research community wide variety production-level benchmark
