Practical blended taint analysis for JavaScript

JavaScript is widely used in Web applications because of its flexibility and dynamic features. However, the latter pose challenges to static analyses aimed at finding security vulnerabilities, (e.g., taint analysis). We present blended taint analysis, an instantiation of our general-purpose analysis framework for JavaScript, to illustrate how a combined dynamic/static analysis approach can deal with dynamic features by collecting generated code and other information at runtime. In empirical comparisons with two pure static taint analyses, we show blended taint analysis to be both more scalable and precise on JavaScript benchmark codes extracted from 12 popular websites at alexa. Our results show that blended taint analysis discovered 13 unique violations in 6 of the websites. In contrast, each of the static analyses identified less than half of these violations. Moreover, given a reasonable time budget of 10 minutes, both static analyses encountered webpages they could not analyze, sometimes significantly many such pages. Case studies demonstrate the quality of the blended taint analysis solution in comparison to that of pure static analysis.