---
type: Article
title: "Thou Shalt Not Depend on Me: Analysing the Use of Outdated JavaScript Libraries on the Web"
description: The study measures client-side JavaScript libraries on more than 133,000 sites using a curated vulnerability catalogue and Chromium inclusion trees. It finds 37% of sites loading a known-vulnerable library and shows that transitive advertising and tracking inclusions are especially likely to be outdated.
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    title: "Thou Shalt Not Depend on Me: Analysing the Use of Outdated JavaScript Libraries on the Web"
    author: Tobias Lauinger, Abdelberi Chaabane, Sajjad Arshad, William Robertson, Christo Wilson, Engin Kirda
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  - Tobias Lauinger
  - Abdelberi Chaabane
  - Sajjad Arshad
  - William Robertson
  - Christo Wilson
  - Engin Kirda
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# Thou Shalt Not Depend on Me: Analysing the Use of Outdated JavaScript Libraries on the Web

**Thou Shalt Not Depend on Me: Analysing the Use of Outdated JavaScript Libraries on the Web** - Tobias Lauinger, Abdelberi Chaabane, Sajjad Arshad, William Robertson, Christo Wilson, Engin Kirda, Internet Society.

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- Original: <https://www.ndss-symposium.org/ndss2017/ndss-2017-programme/thou-shalt-not-depend-me-analysing-use-outdated-javascript-libraries-web/>
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## Content

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Thou Shalt Not Depend on Me: Analysing the Use
     of Outdated JavaScript Libraries on the Web

      Tobias Lauinger, Abdelberi Chaabane, Sajjad Arshad, William Robertson, Christo Wilson and Engin Kirda
                                                               Northeastern University
                                                  {toby, 3abdou, arshad, wkr, cbw, ek}@ccs.neu.edu

    Abstract—Web developers routinely rely on third-party Java-                     scripts or HTML into vulnerable websites via a crafted tag. As
Script libraries such as jQuery to enhance the functionality of                     a result, it is of the utmost importance for websites to manage
their sites. However, if not properly maintained, such dependen-                    library dependencies and, in particular, to update vulnerable
cies can create attack vectors allowing a site to be compromised.                   libraries in a timely fashion.
    In this paper, we conduct the first comprehensive study of                          To date, security research has addressed a wide range of
client-side JavaScript library usage and the resulting security                     client-side security issues in websites, including validation [30]
implications across the Web. Using data from over 133 k websites,
we show that 37 % of them include at least one library with a
                                                                                    and XSS ([17], [36]), cross-site request forgery [4], and session
known vulnerability; the time lag behind the newest release of                      fixation [34]. However, the use of vulnerable JavaScript libraries
a library is measured in the order of years. In order to better                     by websites has not received nearly as much attention. In
understand why websites use so many vulnerable or outdated                          2014, a series of blog posts presented cursory measurements
libraries, we track causal inclusion relationships and quantify                     highlighting that major websites included known vulnerable
different scenarios. We observe sites including libraries in ad hoc                 libraries ([25], [26], [24]). These findings echo warnings from
and often transitive ways, which can lead to different versions of                  other software ecosystems like Android [3], Java [32] and
the same library being loaded into the same document at the same                    Windows [21], which show that vulnerable libraries continue
time. Furthermore, we find that libraries included transitively, or                 to exist in the wild even when they are widely known to
via ad and tracking code, are more likely to be vulnerable. This                    contain severe vulnerabilities. Given that JavaScript dependency
demonstrates that not only website administrators, but also the
dynamic architecture and developers of third-party services are
                                                                                    management is relatively primitive and corresponding tools
to blame for the Web’s poor state of library management.                            are not as well-established as in more mature ecosystems,
                                                                                    these findings suggest that security issues caused by outdated
    The results of our work underline the need for more thorough                    JavaScript libraries on the Web may be widespread.
approaches to dependency management, code maintenance and
third-party code inclusion on the Web.                                                  In this paper, we conduct the first comprehensive study on
                                                                                    the security implications of JavaScript library usage in websites.
                                                                                    We seek to answer the following questions:
                           I.   I NTRODUCTION
    The Web is arguably the most popular contemporary                                  •    Where do websites load JavaScript libraries from (i.e.,
programming platform. Although websites are relatively easy to                              first or third-party domains), and how frequently are
create, they are often composed of heterogeneous components                                 these domains used?
such as database backends, content generation engines, multiple                        •    How current are the libraries that websites are using,
scripting languages and client-side code, and they need to deal                             and do they contain known vulnerabilities?
with unsanitised inputs encoded in several different formats.                          •    Are web developers intentionally including JavaScript
Hence, it is no surprise that it is challenging to secure websites                          libraries, or are these dependencies caused by adver-
because of the large attack surface they expose.                                            tising and tracking code?
                                                                                       •    Are existing remediation strategies effective or widely
    One specific, significant attack surface are vulnerabilities
                                                                                            used?
related to client-side JavaScript, such as cross-site scripting
(XSS) and advanced phishing. Crucially, modern websites often                          •    Are there additional technical, methodological, or
include popular third-party JavaScript libraries, and thus are at                           organisational changes that can improve the security
risk of inheriting vulnerabilities contained in these libraries. For                        of websites with respect to JavaScript library usage?
example, a 2013 XSS vulnerability in the jQuery [13] library                        Note that the focus of this paper is not measuring the security
before version 1.6.3 allowed remote attackers to inject arbitrary                   state of specific JavaScript libraries. Rather, our goal (and
                                                                                    primary contribution) is to empirically examine whether website
Permission to freely reproduce all or part of this paper for noncommercial          operators keep their libraries current and react to publicly
purposes is granted provided that copies bear this notice and the full citation     disclosed vulnerabilities.
on the first page. Reproduction for commercial purposes is strictly prohibited
without the prior written consent of the Internet Society, the first-named author       Answering these questions necessitated solving three funda-
(for reproduction of an entire paper only), and the author’s employer if the        mental methodological challenges. First, there is no centralised
paper was prepared within the scope of employment.                                  repository of metadata pertaining to JavaScript libraries and
NDSS ’17, 26 February - 1 March 2017, San Diego, CA, USA
Copyright 2017 Internet Society, ISBN 1-891562-46-0                                 their versions, release dates, and known vulnerabilities. To
http://dx.doi.org/10.14722/ndss.2017.23414                                          address this, we manually constructed a catalogue containing
all “release” versions of 72 of the most popular open-source                  •    We present results on the origins of vulnerable
libraries, including detailed vulnerability information on a                       JavaScript library inclusions, which allows us to
subset of 11 libraries. Second, web developers often modify                        contrast the security posture of website developers with
JavaScript libraries by reformatting, restructuring or appending                   third-party modules such as WordPress, advertising or
code, which makes it difficult to detect library usage in the                      tracking networks, and social media widgets.
wild. We solve this problem through a combination of static                   •    We show that a large number of websites include
and dynamic analysis techniques. Third, to understand why                          JavaScript libraries in unexpected ways, such as mul-
specific libraries are loaded by a given site, we need to track all                tiple inclusions of different library versions into the
of the causal relationships between page elements (e.g., script                    same document, which may impact their attack surface.
s1 in frame f1 injects script s2 into frame f2 ). To solve this,              •    We find existing remediation strategies to be ineffective
we developed a customised version of Chromium that records                         at mitigating the threats posed by vulnerable JavaScript
detailed causality trees of page element creation relationships.                   libraries. For example, less than 3 % of websites could
    Using these tools, we crawled the Alexa Top 75 k websites                      fix all their vulnerable libraries by applying only patch-
and a random sample of 75 k websites drawn from a snapshot                         level updates. Similarly, only 1 % of websites use the
of the .com zone in May 2016. These two crawls allow us to                         version-aliasing services offered by JavaScript CDNs.
compare and contrast JavaScript library usage between popular
and unpopular websites. In total, we observed 11,141,726 inline                                  II.   BACKGROUND
scripts and script file inclusions; 87.7 % of Alexa sites and
                                                                              JavaScript has allowed web developers to build highly inter-
46.5 % of .com sites used at least one well-known JavaScript
                                                                          active websites with sophisticated functionality. For example,
library, with jQuery being the most popular by a large majority.
                                                                          communication and production-related online services such as
    Analysis of our dataset reveals many concerning facts                 Gmail and Office 365 make heavy use of JavaScript to create
about JavaScript library management on today’s Web. More                  web-based applications comparable to their more traditional
than a third of the websites in our Alexa crawl include at                desktop counterparts. In this paper, we focus exclusively on
least one vulnerable library version, and nearly 10 % include             aspects of client-side JavaScript executed in a browser, not the
two or more different vulnerable versions. From a per-library             recent trend of using JavaScript for server-side programming.
perspective, at least 36.7 % of jQuery, 40.1 % of Angular, 86.6 %
of Handlebars, and 87.3 % of YUI inclusions use a vulnerable              A. JavaScript Libraries
version. Alarmingly, many sites continue to rely on libraries
like YUI and SWFObject that are no longer maintained. In fact,                In many cases, to make their lives easier, web developers
the median website in our dataset is using a library version              rely on functionality that is bundled in libraries. For example,
1,177 days older than the newest release, which explains why so           jQuery [13] is a popular JavaScript library that makes HTML
many vulnerable libraries tend to linger on the Web. Advertising,         document traversal and manipulation, event handling, animation,
tracking and social widget code can cause transitive library              and AJAX much simpler and compatible across browsers.
inclusions with a higher rate of vulnerability, suggesting that               In the simplest case, a JavaScript library is a plain-text script
these problems extend beyond individual website administrators            containing code with reasonably well-defined functionality. The
to providers of Web infrastructure and services.                          script has full access to the DOM that includes it; the concept of
    We also observe many websites exhibiting surprising                   namespaces does not exist in JavaScript, and everything that is
behaviours with respect to JavaScript library inclusion. For              created is by default global. More elaborate libraries use hacks
example, 4.2 % of websites using jQuery in the Alexa crawl                and conventions to protect the code against naming conflicts,
include the same library version multiple times in the same               and expose interfaces for retrieving meta-data such as the name
document, and 10.9 % include multiple different versions of               and version of the library. Over the course of this study, we
jQuery into the same document. To our knowledge, ours                     found that JavaScript libraries overwhelmingly use the Semantic
is the first study to make these observations, since existing             Versioning [28] convention of major.minor.patch, such
tools ([27], [20]) are unable to detect these anomalies. These            as 1.0.1, where the major version component is increased for
strange behaviours may have a negative impact on security as              breaking changes, the minor component for new functionality,
asynchronous loading leads to nondeterministic behaviour, and             and the patch component for backwards-compatible bug fixes.
it remains unclear which version will ultimately be used.                     To include a library into their website, developers typically
    Perhaps our most sobering finding is practical evidence that          use the <script src="url"></script> HTML tag and
the JavaScript library ecosystem is complex, unorganised, and             point to an externally-hosted version of the library or a copy
quite “ad hoc” with respect to security. There are no reliable            on their own server. Library vendors often provide a minified
vulnerability databases, no security mailing lists maintained by          version that has comments and whitespace removed and local
library vendors, few or no details on security issues in release          variables shortened to reduce the size of the file. Developers
notes, and often, it is difficult to determine which versions of          can also concatenate multiple libraries into a single file, create
a library are affected by a specific reported vulnerability.              custom builds of libraries, or use advanced minification features
                                                                          such as dead code removal. While custom minification builds
   Overall, our study makes the following contributions:                  are relatively common, more aggressive minification settings are
                                                                          rare in client-side JavaScript because they can break code [9].
   •    We conduct the first comprehensive study showing that
        a significant number of websites include vulnerable or            CDNs.     Many libraries are available on Content Distribution
        outdated JavaScript libraries.                                    Networks (CDNs) for use by other websites. Google, Microsoft

                                                                      2
                                                                             TABLE I.     T HE 30 MOST FREQUENT LIBRARIES IN OUR A LEXA WEB
and Yandex host libraries on their CDNs, some popular libraries             CRAWL ( OUT OF 72 SUPPORTED LIBRARIES ). ∗ DYNAMIC METHOD DETECTS
(e.g., Bootstrap and jQuery) offer their own CDNs, and some                  l ALL KNOWN VERSIONS , w RECENT VERSIONS , m ONLY THE LIBRARY ’ S
community-based CDNs accept to host arbitrary open-source                        PRESENCE ; OTHERWISE DETECTION BY STATIC METHOD ONLY.
libraries. JavaScript CDNs enable caching of libraries across
                                                                                                  Versions     Bower   Wapp   Use on Crawled Sites
websites to increase performance. Another useful feature offered              Library                ∗
                                                                                                       Dyn.     Rank     %    A LEXA        C OM
by some CDNs is version aliasing. That is, when including a
                                                                              jQuery               66     l        1   42 %   84.5 %        62.6 %
library, the developer may specify a version prefix instead of the            jQuery-UI            46     l       13    7%    24.7 %         8.6 %
full version string, in which case the CDN returns the newest                 Modernizr            24     l       18   10 %   22.1 %         9.3 %
available version with that prefix. When implemented correctly,               Bootstrap            32     w        3          13.7 %         4.8 %
                                                                              Yepnope              10     m             6%    13.6 %         5.3 %
the patched version of a library will automatically be used on the            jQuery-Migrate        7     l                   11.3 %        10.7 %
website when it becomes available on the CDN. However, this                   SWFObject             2     w                   10.7 %         5.2 %
                                                                              Underscore           61     l       12    3%     5.8 %         2.4 %
works only for security issues fixed in a backwards-compatible                jQuery-Tools          8     w                    4.0 %         1.2 %
manner, and it conflicts with client-side security mechanisms                 Flexslider           11     m                    3.6 %         1.6 %
such as subresource integrity [37]. In addition, version aliasing             Moment               54     l        6           3.5 %         1.4 %
                                                                              RequireJS            62     l                    3.4 %         2.3 %
makes client-side caching of resources less efficient because it              jQuery-Form          14                          2.7 %         3.4 %
must be configured for shorter time spans, that is, hours instead             Backbone             29     l             2%     2.7 %         1.6 %
of years. As a result, version aliasing is often discouraged [11].            Angular             110     w        2           2.5 %         1.6 %
                                                                              LoDash               77     w       26           2.4 %         2.5 %
                                                                              jQuery-Fancybox      10                          2.3 %         1.4 %
Third Parties.        Third-party modules such as advertising,                GreenSock GSAP       45                          2.3 %         2.8 %
trackers, social media or other widgets that are often embedded               Handlebars           25     l                    2.2 %         0.5 %
                                                                              Prototype             5     l                    2.2 %         1.3 %
in webpages typically implemented in JavaScript. Furthermore,                 Hammer.js            26     w                    1.9 %         1.1 %
these scripts can also load libraries, possibly without the                   FastClick            33     m       23           1.7 %         0.4 %
                                                                              WebFont Loader      100                          1.7 %         1.3 %
knowledge of the site maintainer. If not isolated in a frame,                 MooTools             27     l             3%     1.6 %         1.4 %
these libraries gain full privileges in the including site’s context.         Isotope              38                          1.6 %         1.8 %
Thus, even if a web developer keeps own library dependencies                  jQuery-Cookie         8                          1.4 %         0.2 %
                                                                              Knockout             21     l                    1.4 %         0.3 %
updated, outdated versions may still be included by badly                     jQuery-Mobile        16     w                    1.4 %         0.8 %
maintained third-party content. Also, some JavaScript libraries               Mustache             29     l                    1.3 %         1.0 %
and many web frameworks contain their own copies of libraries                 jQuery-Validation    13                          1.1 %         0.6 %

they depend on. Hence, web developers may unknowingly rely
on software maintainers to update JavaScript libraries.
                                                                            HTML <tag> anywhere in the string as HTML [14], so that a
                                                                            parameter such as #<img src=/ onerror=alert(1)>
B. Vulnerabilities in JavaScript Libraries                                  would lead to code execution rather than a selection. This
    While JavaScript is the de-facto standard for developing                behaviour was considered a vulnerability and fixed.
client-side code on the Web, at the same time it is notorious                   Other vulnerabilities in JavaScript libraries include cases
for security vulnerabilities. A common, lingering problem is                where libraries do not sanitise inputs that are expected to be
Cross-Site Scripting (XSS) [17], which allows an attacker to                pure text, but are passed to eval() or document.write()
inject malicious code (or HTML) into a website. In particular,              internally, which could cause them to be executed as script
if a JavaScript library accepts input from the user and does                or rendered as markup, respectively. Attackers can use these
not validate it, an XSS vulnerability might creep in, and all               capabilities to steal data from a user’s browsing session, initiate
websites using this library could become vulnerable.                        transactions on the user’s behalf, or place fake content on a
    As an example, consider the popular jQuery library and its              website. Therefore, JavaScript libraries must not introduce any
$() function, which is overloaded and has different behaviour               attack vectors into the websites where they are used.
depending on which type of argument is passed [15]: If a string
containing a CSS selector is passed, the function searches the                                     III.       M ETHODOLOGY
DOM tree for corresponding elements and returns references to                   Identifying client-side JavaScript libraries, finding out how
them; if the input string contains HTML, the function creates               they are loaded by a website, and determining whether they are
the corresponding elements and returns the references. As a                 outdated or vulnerable requires a combination of techniques
consequence, developers who pass improperly sanitised input                 and data sources. Challenges arise due to the lax JavaScript
to this function may inadvertently allow attackers to inject                language, the fragmented library ecosystem, and the complex
code even though the developers’ original intent was only                   nature of modern websites. First, we need to collect metadata
to select an existing element. While this API design places                 about popular JavaScript libraries, including a list of available
convenience over security considerations and the implications               versions, the corresponding release dates, code samples, and
could be better highlighted in the documentation, it does not               known vulnerabilities. Second, we must be able to determine if
automatically constitute a vulnerability in the library.                    JavaScript code found in the wild is a known library. Third, we
                                                                            need to crawl websites while keeping track of causal resource
    In older versions of jQuery, however, the $() function’s le-
                                                                            inclusion relationships and match them with detected libraries.
niency in parsing string parameters could lead to complications
by misleading developers to believe, for instance, that any string
                                                                            A. Catalogueing JavaScript Libraries
beginning with # would be interpreted as a selector and could be
safe to pass to the function, as #test selects the element with                In contrast to Maven’s Central Repository in the Java world,
identifier test. Yet, jQuery considered parameters containing a             JavaScript does not have a similarly popular repository of library

                                                                        3
versioning and project dependency metadata. We must therefore                                                       0            2           4
collect and correlate this data from various separate sources.                                                      1            3           5
                                                                                Angular (110)
    1) Selecting Libraries: The initial construction of our meta-              Backbone (29)
data archive involves a certain amount of manual verification                       Dojo (72)
work. Since there are thousands of JavaScript libraries (e.g.,                    Ember (77)
the community-based cdnjs.com hosts 2,379 projects as of                      Handlebars (25)
August 2016), we focus our study on the most widely used                          jQuery (66)
                                                                            jQuery-Migrate (7)
libraries because they are the most consequential.
                                                                           jQuery-Mobile (16)
     To select libraries, we leverage library popularity statistics            jQuery-UI (46)
provided by the JavaScript package manager Bower [6] and                       Mustache (29)
the web technology survey Wappalyzer [38]. We extend this                          YUI 3 (37)
list of popular libraries with all projects hosted on the public                             0.0        0.2        0.4          0.6         0.8      1.0
                                                                                                               Fraction of total versions
CDNs operated by Google, Microsoft and Yandex. As we will
show in Section IV-C, many websites rely on these commercial
                                                                          Fig. 1. Fraction of library versions with i distinct known vulnerabilities
CDNs to host JavaScript libraries. We collected the data from             each (represented by colours), out of the total library versions in parentheses.
Bower, Wappalyzer, and the three CDNs in January 2016.                    Angular 1.2.0 has 5 known vulnerabilities and there are 110 versions overall.
    Due to various data availability requirements explained in
detail in Section III-A5, we need to exclude certain libraries
from our study. Overall, we support 72 libraries—18 out of the            ajax.googleapis.com/ajax/libs/jquery/{version}/jquery.min.js. In
Top 20 installed with Bower, 7 out of the Top 10 frameworks               doing so, we make sure that we download all available variants
identified on websites by Wappalyzer, 13 of the 14 libraries              of a library file, including the full development variant and the
hosted by Google, 12 of the 18 libraries hosted by Microsoft,             minified production variant without whitespace or comments.
and all 11 libraries hosted by Yandex. Table I shows a subset                 When comparing files downloaded from official websites
of 30 libraries in our catalogue as well as their rank on Bower           and different CDNs, we noticed that even the same version
and their market share according to Wappalyzer. Although our              and variant (e.g., minified) of a library may sometimes differ
catalogue appears to cover a sparse set of the libraries on               between sources. We observed additional whitespace, removal
Bower, many of the missing ranks belong to submodules of                  of comments, or the likely use of a different minifier or minifier
popular libraries (e.g., rank 5 is Angular Mocks). According to           setting, especially when the library’s developers do not provide a
Wappalyzer, we cover 73 % of the most popular libraries.                  minified version. This observation highlights the importance of
    2) Extracting Versioning Information: Our next step is                collecting ground-truth JavaScript library samples from as many
compiling a complete list of library versions along with                  official and semi-official sources as possible. Therefore, we use
their release dates. After unsuccessful experiments with file             official websites as well as dedicated CDNs (Bootstrap CDN
timestamps and available-since dates on the libraries’ official           and jQuery CDN), commercial CDNs (Google, Microsoft, and
websites and CDNs, we determined that GitHub was the most                 Yandex), and open source CDNs (jsDelivr, cdnjs and OssCDN).
reliable source for this kind of information. Nearly all of the               In total, we collect 81,027 JavaScript files. We analyse
open source libraries in our seed lists are hosted on GitHub              the sizes of the “main” files of each library in our dataset
and tag the source code of their releases, allowing us to extract         (that is, we exclude files such as plug-ins that cannot be
timestamps and version identifiers from the tags. In naming               used stand-alone), and find that Script.aculo.us 1.9.0 is the
their releases, they typically follow a major.minor.patch                 smallest at 996 bytes (minified). After accounting for duplicates
version numbering scheme, which makes it straightforward to               and discarding files smaller than 996 bytes (to reduce the
identify tags pertaining to releases and ignore all other tags,           likelihood of false positives due to shared ancillary resources
including “alpha,” “beta” and “release candidate” versions that           such as configuration files, localisations and plug-ins), our final
are not meant to be used in production. As shown in Table I,              catalogue includes 19,099 distinct files.
popular libraries like Angular and jQuery have up to 110 and
66 distinct versions in our catalogue, respectively. However,                  4) Identifying Vulnerabilities: The last step towards building
half of the libraries have fewer than 26 versions.                        our catalogue is aggregating vulnerability information for our
                                                                          72 JavaScript libraries. Unfortunately, there is no centralised
    3) Obtaining Reference Files: Some methods of library                 database of vulnerabilities in JavaScript libraries; instead, we
detection require us to have access to code samples for each              manually compile vulnerability information from the Open
version of a library. We gather library code from two sources:            Source Vulnerability Database (OSVDB), the National Vulnera-
the official website of each library, and from CDNs. For the              bility Database (NVD), public bug trackers, GitHub comments,
official websites, we manually download all available library             blog posts, and the vulnerabilities detected by Retire.js [27].
versions. However, some official websites do not provide copies
of old library versions, or they only provide copies of a subset              Overall, we are able to obtain systematically documented
of versions. In contrast, CDNs typically do host comprehensive            details of vulnerabilities for 11 of the JavaScript libraries in
collections of old library versions in order to not break websites        our catalogue. In some cases, the documentation for a given
that depend on older versions. We utilise the API of one such             flaw specifies an affected range of versions, in which case we
CDN, jsDelivr, to automatically discover all available versions           consider all library versions within the range to be vulnerable.
of libraries on five supported CDNs. For the remaining CDNs,              In other cases, when a flaw is identified in a specific version v
we construct download link templates manually, such as https://           of a library, we consider all versions ≤ v to be vulnerable.

                                                                      4
    Figure 1 shows details of the 11 libraries with vulnerability          global variable that can be detected at runtime. Furthermore,
information. For each library, we show the total number of                 most libraries in our catalogue contain a variable or method
versions in our catalogue as well as the fraction of versions              that returns the version of the library. As an illustration, the
with i distinct known vulnerabilities. The worst offender is               following snippet of JavaScript code detects jQuery:
Angular 1.2.0, which contains 5 vulnerabilities. Overall, we           1 v a r j q = window . j Q u e r y | | window . $ | | window . $ j q | |
see that 28.3 %, 6.7 %, and 6.1 % of these library versions                      window . $ j ;
contain one, two, or three known vulnerabilities, respectively.        2 i f ( j q && j q . f n ) {
                                                                       3       r e t u r n j q . f n . j q u e r y | | n u l l ; / / v e r s i o n ( i f known )
    5) Limitations: Although we have expended a great deal of          4 } else {

effort constructing our catalogue of JavaScript libraries, it is       5       r e t u r n f a l s e ; / / jQuery not found
                                                                       6 }
impacted by several limitations. First, by choosing GitHub for
versioning and release date information, we need to exclude a              Line 1 extracts jQuery’s global variable, and line 3 returns the
small number of libraries that have few or no releases tagged on           version number if it exists in its fn.jquery attribute. Note
GitHub or do so in an apparently inconsistent way (e.g., multiple          that in order to prevent false positives, we check for the global
successive releases tagged on the same day). Furthermore, we               variable and that the fn attribute exists.
cannot include closed-source libraries such as Google Maps,
advertising and tracking libraries like Google Analytics, and                  While this dynamic methodology detects libraries even if the
social widgets since they typically do not publish version                 source code has been (lightly) modified, it cannot extract version
information. Fortunately, the vast majority of such libraries are          numbers if they are not programmatically exported by the
hosted by their creators at a single, non-versioned URL (e.g.,             library. Overall, we collect or create signatures for 68 libraries,
https://www.google-analytics.com/analytics.js), meaning that               out of which seven do not export any version information, and
all clients automatically include the latest version of the library.       a few more provide only partial version information, as shown
                                                                           in Table I. Typically, more recent versions of a library export
    Second, our catalogue may miss some revisions of libraries             version information while older versions may not.
if the author chose to patch the code and not increment the
version number. Similarly, we may miss revisions if they are                   Limitations: Our two detection techniques represent a best-
denoted using non-standard notation, such as special suffixes,             effort approach to identifying JavaScript libraries in the wild.
four-part version numbers, etc., and we may not possess                    However, there are cases where both techniques can fail. For
any code samples for a version of a library if it cannot be                example, heavily modified libraries will not match our file
downloaded from the developer website or a supported CDN.                  hashes nor will they match the dynamic signatures. Furthermore,
                                                                           we rely on the correctness of our information sources, i.e., that
    Third, our library vulnerability assessments are based solely          CDNs contain the version of a library that they claim, and that
on publicly available documentation. We make no attempts to                libraries export the correct version string and do not attempt to
discover new vulnerabilities, or to quantify the exploitability            conceal their presence. Effectively, these limitations mean that
of libraries as used on websites, for both practical and ethical           our measurement results should be viewed as lower bounds.
reasons. Thus, although a website may include a vulnerable
library, this does not necessarily imply that the website is
                                                                           C. Data Collection
exploitable. Furthermore, libraries differ in their release cycles,
attack surfaces, functionality, and public scrutiny with respect               A central contribution of our work is to analyse not only
to vulnerabilities. Thus, we do not claim to provide comparable            whether outdated libraries are being used, but why this may
coverage of vulnerabilities for each library in our catalogue.             be the case. This implies that detecting whether a library
                                                                           exists in a window or frame is not enough; we must also
B. Library Identification                                                  detect if it was loaded by another script. To model causal
                                                                           inclusion relationships of resources in websites, we introduce
    Identifying an unknown file as a specific version of a                 the theoretical concept of causality trees and implement it
JavaScript library is challenging because these libraries are text,        in a modern browser. We integrate our two library detection
which gives web developers, development tools and network                  methods into this modified browser environment and use it to
software the ability to modify them, e.g., by adding or removing           collect data about the usage of JavaScript libraries on the Web.
features, concatenating multiple libraries into a single file, or
tampering with comments. To reliably detect as many libraries                  Causality Trees: The goal of a causality tree is to represent
as possible, we use two complementary techniques. These                    the causal element creation relationships that occur during
techniques are conceptually similar to those used by the Library           the loading and execution of a dynamic website in a modern
Detector Chrome extension [20] and Retire.js.                              browser. A causality tree contains a directed edge A → B
                                                                           if and only if element A causes element B to load. More
   Static Detection: We compute the file hashes of all observed            specifically, the elements we model include scripts, images
JavaScript code and compare them to the 19,099 reference                   and other media content, stylesheets, and embedded HTML
hashes in our catalogue. File hashing enables us to identify all           documents. A relationship exists whenever an element creates
cases where libraries are used “as-is.”                                    another element (e.g., a script creates an iframe) or changes an
                                                                           existing element’s URL (e.g., a script changes the URL of an
    Dynamic Detection: During the crawl, we detect the                     iframe or redirects the main document), which is equivalent to
presence of libraries in the browser by fingerprinting the                 creating a new element with a different URL.
JavaScript runtime environment and by relying on libraries
to identify themselves. Specifically, modern libraries typically               While the nodes in a causality tree correspond to nodes
make themselves available to the environment by means of a                 in the website’s DOM, their structure is entirely unrelated to

                                                                       5
Ad Frame w/ Scripts                                                       Chrome Debugging Protocol [8] to minimise the necessity for
                              Ad Script              Inline Script        brittle browser source code modifications.
                                                                             The Chrome Debugging Protocol provides programmatic
                                                                          access to the browser and allows clients to attach to open win-
                                                                          dows, inspect network traffic, and interact with the JavaScript
 Included Scripts                         Root             Frame          environment and the DOM tree loaded in the window. Two
                                                                          prominent uses of this API are the Chrome Developer Tools (an
Fig. 2.   Example causality tree.
                                                                          HTML and JavaScript front-end to the protocol) and Selenium’s
                                                                          WebDriver interface to remotely control Chrome.
the hierarchical DOM tree. Rather, nodes in the causality tree                At a high level, we generate causality trees by observing
are snapshots of elements in the DOM tree at specific points              resource requests through the network view of the debugging
in time, and may appear multiple times if the DOM elements                protocol. Note that this view includes resources not actually
are repeatedly modified. For instance, if a script creates an             loaded over the network, e.g., inline URL schemas such as
iframe with URL U1 and later changes the URL to U2 , the                  data: or javascript:. We disable all forms of caching
corresponding script node in the causality tree will have two             to observe even duplicate resource inclusions within the same
document nodes as its children, corresponding to URLs U1                  frame, which are otherwise handled through an in-memory
and U2 , but referring to the same HTML <iframe> element.                 cache. For each loaded resource, the protocol allows us to
Similarly, the predecessor of a node in the causality tree is not         identify the frame in which the resource is located as well
necessarily a predecessor of the corresponding HTML element               as the initiating script, where applicable. Similarly, we utilise
in the DOM tree; they may even be located in two different                protocol methods to be notified of script generation events and
HTML documents, such as when a script appends an element                  store the source code of both inline and URL-based JavaScript.
to a document in a different frame.                                       (This includes source code from attribute-based event handlers
                                                                          and string evaluation, which we both model as inline script
     Figure 2 shows a synthetic example of a causality tree.              nodes.) We store a log of all relevant events during the crawl
The large black circle is the document root (main document),              and assemble the causality trees in a post-processing step.
filled circles are scripts, squares are HTML documents (e.g.,
embedded in frames or corresponding to a new main document                    Integration of Library Detection: Hash-based detection of
if there is a top-level redirect), and empty circles are other re-        libraries is relatively straight-forward to integrate with our
sources (e.g., images). Edges denote “created by” relationships;          crawler; we simply compute the source code hashes of all
for example, in Figure 2 the main document included the grey              script nodes in the causality trees and look them up in our
script, which in turn included the blue script. Dashed lines              reference catalogue during post-processing.
around nodes denote inline scripts, while solid lines denote                  Integrating dynamic library detection is more challenging—
scripts included from an URL. Thick outlines denote that a                out of the box, existing detection methods can only detect
resource was included from a known ad network, tracker, or                whether a JavaScript context (window or frame) contains a
social widget (see below for more details).                               library or not, but in general this information is not sufficient
                                                                          to properly label the correct script node in the causality tree.
     The colour of nodes in Figure 2 denotes which document               The Chrome Debugging Protocol allows us to link a method
they are attached to in the DOM: grey corresponds to resources            executed in a JavaScript context to the script that contains its
attached to the main document, while we assign one of four                implementation; thus, once we hold a reference to a JavaScript
colours to each further document in frames. Document squares              library object (such as jq in the example from Section III-B),
contain the colour of their parent location in the DOM, and               we dynamically enumerate its instance methods and use an
their own assigned colour. Resources created by a script in               arbitrary one of them to identify the implementing script.
one frame can be attached to a document in another frame, as
shown by the grey script which has a blue child in Figure 2,                  Another challenge in dynamically detecting libraries during
i.e., the blue script is a child of the blue document in the DOM.         the crawl is that we need to inject the detection code into each
                                                                          frame of a website, since each frame has its own JavaScript
    Figure 12 shows the causality tree of mercantil.com,                  scope and may contain independent library instances. This is
with images and other irrelevant node types omitted for clarity.          further complicated by the fact that modern websites are quite
It includes three clearly visible social media widgets: Twitter,          dynamic and an ad frame, for instance, may quickly navigate
Facebook, and LinkedIn. Note that the web developer embedded              to a different URL, which causes any library previously loaded
code provided by the social networks into the main document,              in the frame to be unloaded or replaced. Lastly, we observe
which in turn initialises each widget and creates one or more             that many websites include multiple copies of the same library,
frames for their contents. We also see that the causality tree            including different versions of the same library (refer to our
includes multiple copies of jQuery in the main document, which            analysis in Section IV-G for details). Typically, only one library
we will discuss in detail in Section IV-G.                                instance can exist in each context because the more recently
                                                                          loaded instance replaces the previous global reference.
   Implementation of Causality Trees: Our definition of
causality trees is related to the concepts previously used to                 In order to be able to study these phenomena in detail and
analyse malicious JavaScript inclusions [1] and cookie matching           also address the other aforementioned detection challenges, we
between online ad exchanges [5], but it differs in the details. Our       inject the detection code into each frame and execute it every
implementation is independent from the aforementioned works               4 seconds. Note that it would not be feasible from a performance
and uses a different technological approach by building on the            point of view to execute the detection code after each script

                                                                      6
creation event since websites routinely contain thousands of               version of each loaded library, which enables us to assess the
script nodes (see Section IV-B).                                           accuracy of the dynamic detection.
    To detect cases of inclusions that we may miss due to                      Overall, we observe that the dynamic detection code is
the four-second detection interval, we additionally execute the            able to identify the exact name and version of 79.2 % of the
dynamic detection code post hoc on all scripts found during                libraries, as well as the name (but not the version) of 18.6 %
the crawl. This execution is done in an individual Node.js                 of the libraries. Only 2 % of libraries fail to be identified (i.e.,
environment with a fake DOM tree. This offline detection                   were false negatives). We manually examine the libraries that
step cannot fully replace in-browser detections, however, since            are only detected by name, and find that the vast majority are
some libraries such as jQuery UI have code or environment                  older versions that do not include a variable or method that
prerequisites that cause the offline detection to fail.                    returns the library version. Of course, 100 % of these libraries
                                                                           can be detected based on their file hashes, which reinforces the
     Annotation of Ads, Trackers and Widgets: To further clarify           importance of using multiple techniques to identify libraries.
the provenance of scripts we observe in our crawl, we aim
to determine whether they are related to known advertising,                    Static Detection: To investigate the efficacy of library
tracking, or social widget code. We achieve this by injecting              detection using file hashes, we conduct a second controlled
a customised version of the AdBlock browser extension into                 experiment: we randomly select from our crawled data 415
each frame. Our version of AdBlock flags content but permits               unique scripts that the dynamic detection code classifies as
it to load, and we verified that our customised version remains            being jQuery, and attempt to detect them based on their file
undetected by common ad-block detection scripts. We use                    hashes. In this case, we are treating the output of the dynamic
EasyList and EasyPrivacy to identify advertising and tracking              detection method as ground truth.
content, and Fanboy’s Social Blocking List to detect social                    Overall, we observe that only 15.4 % of the libraries can
network widgets. For our analysis, we label an element in                  be identified as jQuery based on their file hash. Although this
the causality tree as ad/tracker/widget-related whenever the               is a low detection rate, the result also matches our expectation
corresponding element or any parent in the DOM tree is labeled             that developers often deploy customised versions of libraries.
by AdBlock. Additionally, we propagate these labels downwards              For example, 90 % of the jQuery libraries that we fail to detect
to all children of the labelled node in the causality tree.                via file hashing contain fewer than 150 line break characters,
    Crawl Parameters: To gain a representative view of                     whereas non-minified copies of jQuery from our catalogue
JavaScript library usage on the Web, we collected two different            contain more than 1900. This strongly suggests that the unique
datasets. First, we crawled the Alexa Top 75 k domains, which              scripts are custom-minified versions of jQuery.
represent websites popular with users. Second, we crawled                      Hypothetical “Name-in-URL” Detection: For the last
75 k domains randomly sampled from a snapshot of the .com                  validation step, we consider a simple library detection heuristic.
zone, that is, a random sample of all websites with a .com                 The heuristic flags a script file as jQuery, for instance, whenever
address, which we expect to be dominated by less popular                   the string “jquery” appears in the URL of the script. To evaluate
websites. We conducted the two crawls in May 2016 from IP                  the accuracy of this heuristic, we extract from our A LEXA crawl
addresses in a /24 range in the US. We observe ∼5 % and                    the set of script URLs that contain “jquery,” and the URLs of
∼17.2 % failure rates in A LEXA and C OM, leaving us with                  scripts detected as jQuery by our dynamic and static methods.
data from 71,217 and 62,086 unique domains, respectively.                  Out of these URLs, 22.3 % contain “jquery” and are also
Failures were due to timeouts and unresolvable domains, which              detected as the library; 69 % are flagged only by the heuristic,
is expected especially for C OM since the zone file contains               and 8.8 % are detected only by our dynamic and static methods.
domains that may not have an active website.                               The heuristic appears to cause a large number of false positives
     To preserve the fidelity of our data collection, our crawler is       due to scripts named “jquery” without containing the library,
based on Chromium and includes support for Flash. We disable               and it also seems to suffer from false negatives due to scripts
various security mechanisms such as malware and phishing                   that contain jQuery but have an unrelated name.
filters. We only crawl the homepage of each visited site due                   We validate this finding by manually examining 50 scripts
to the presence of many sites that thwart deeper traversal by              from each of the two set differences. The scripts in the
requiring log-ins. While visiting a page, the crawler scrolls              detection-only sample appear to contain additional code such
downwards to trigger loading of any dynamic content. As we                 as application code or other libraries. Only one of these scripts
found page-loaded events to be unreliable, our crawler remains             does not contain jQuery but Zepto.js, an alternative to jQuery
on each page for a fixed delay of 60 seconds before clearing               that is partially compatible and also defines the characteristic
its entire state, restarting, and then proceeding to the next site.        $.fn variable. On the other hand, none of the scripts in the
                                                                           heuristic-only sample can be confirmed as the library; nearly
D. Validation                                                              all of them contain plug-ins for jQuery but not the library itself.

    As the final step in our methodology, we validate that our                 The results for the Modernizr library, which does not have
static and dynamic detection methods work in practice.                     an equivalent to jQuery’s extensive plug-in ecosystem, confirm
                                                                           this trend. The overlap between heuristic and detection is 55.3 %
    Dynamic Detection: To investigate the efficacy of our                  of URLs, heuristic-only 0.8 %, and detection-only 44 %—the
dynamic detection code, we conduct a controlled experiment:                simple heuristic misses many Modernizr files renamed by
we load each of the reference libraries in our catalogue into              developers. These results underline the need for more robust
Node.js, one at a time, and attempt to detect each file with               detection techniques such as our dynamic and static methods;
the dynamic detection method. Intuitively, we know the exact               we do not use the heuristic in our analysis.

                                                                       7
                    1.0                                                               1.0                                                                               1.0
                                                     Internal
                                                     External
                    0.8                              Inline                           0.8                                                                               0.8




                                                                  CDF (Alexa sites)




                                                                                                                                                    CDF (Alexa sites)
CDF (Alexa sites)




                    0.6                                                               0.6                                                                               0.6


                    0.4                                                               0.4                                                                               0.4

                                                                                                                                    Inline
                    0.2                                                               0.2                                           External                            0.2
                                                                                                                                    Internal                                                                          Vulnerable
                                                                                                                                    All                                                                               All
                    0.0 0                                                             0.0                                                                               0.0
                      10    101   102      103     104      105                             0   2   4        6       8         10    12        14                             0   2   4        6       8         10      12        14
                                    # scripts                                                           # detected libraries                                                              # detected libraries


Fig. 3. Distribution of JavaScript inclusion type                 Fig. 4.    Distribution of library inclusion type                                 Fig. 5. Distribution of vulnerable library count
frequency per site in A LEXA.                                     frequency per site in A LEXA.                                                     versus overall library count per site in A LEXA.

                    1.0                                                               1.0                                                                               1.0
                                                     Internal
                                                     External
                    0.8                              Inline                           0.8                                                                               0.8
                                                                  CDF (com sites)




                                                                                                                                                    CDF (com sites)
CDF (com sites)




                    0.6                                                               0.6                                                                               0.6


                    0.4                                                               0.4                                                                               0.4

                                                                                                                                    Inline
                    0.2                                                               0.2                                           External                            0.2
                                                                                                                                    Internal                                                                          Vulnerable
                                                                                                                                    All                                                                               All
                    0.0 0                                                             0.0                                                                               0.0
                      10    101   102      103     104      105                             0   2   4        6       8         10    12        14                             0   2   4        6       8         10      12        14
                                    # scripts                                                           # detected libraries                                                              # detected libraries


Fig. 6. Distribution of JavaScript inclusion type                 Fig. 7.    Distribution of library inclusion type                                 Fig. 8. Distribution of vulnerable library count
frequency per site in C OM.                                       frequency per site in C OM.                                                       versus overall library count per site in C OM.


                                         IV.     A NALYSIS                                                        Since paths correspond to causal relationships, intuitively this
                                                                                                                  means that the inclusion of a node could have been influenced
    In this section, we analyse the data from our web crawls.
                                                                                                                  by up to 438 predecessors. In both crawls, images tend to
First, we present a general overview of the dataset by drilling
                                                                                                                  appear further up in the causality trees at a median depth of 1,
down into the causality trees, overall JavaScript inclusion
                                                                                                                  that is, at least half of them are directly included in the main
statistics, and vulnerable JavaScript library inclusions. Next, we
                                                                                                                  document, whereas documents tend to appear further down
examine risk factors for sites that include vulnerable libraries,
                                                                                                                  at a median depth of 2, which indicates that they are more
and the age of vulnerable libraries (i.e., relative to the latest
                                                                                                                  frequently dynamically generated.
release of each library). Finally, we examine unexpected,
duplicate library inclusions in websites, and investigate whether
common remediations practices are useful and used in practice.                                                    B. General JavaScript Statistics
                                                                                                                      Scripts are the most common node type in our causality
A. Causality Trees                                                                                                trees; 97 % of A LEXA sites and 83.6 % of C OM sites contain
    We begin our analysis by measuring the complexity of the                                                      JavaScript. The most common script type are inline scripts,
websites in our crawls. The median causality tree in A LEXA                                                       which includes script code embedded as text in a <script>
contains 133 nodes (p95: 425, max: 19,508) whereas it is                                                          tag, any code in attribute-based event handlers (such as the
38 nodes (p95: 298, max: 25,485) in C OM, indicating that                                                         onclick attribute), and any code evaluated from strings using
A LEXA contains a larger share of more complex websites.                                                          methods such as eval(). The causality trees from the A LEXA
Similarly, A LEXA and C OM contain a median of 52 and                                                             and C OM crawls contain a median of 24 and 9 inline scripts,
14 image nodes, 44 and 16 script nodes, and 4 and 2 document                                                      respectively, with 5 % of the sites having hundreds of inline
nodes per causality tree, respectively. 95 % of frames in both                                                    scripts—the maximum observed was 19 K and 25 K.
crawls show only one or two documents during the 60 s of
                                                                                                                     When looking at URL-based script inclusions, we distin-
our crawl, whereas a few cycle through larger numbers of
                                                                                                                  guish between internal scripts, that is, code hosted on the
documents (max: 12 for A LEXA and 32 for C OM).
                                                                                                                  same domain as the website (or a subdomain of the website),
    The median depth of the causality trees (defined as the                                                       and all other scripts that we call external. Figures 3 and 6
length of the longest path from the root to a leaf) is 4 inclusions                                               depict the distribution of script inclusion types for A LEXA and
(p95: 9, max: 438) in A LEXA and 3 (p95: 8, max: 62) in C OM.                                                     C OM, respectively. About 91.7 % of all A LEXA sites include

                                                                                                             8
    TABLE II.      E XTERNAL JAVA S CRIPT: T OP 10 MARKET SHARE .         TABLE IV.       I NCLUSION TYPES OF DETECTED LIBRARIES : LOADED FROM
S ORTED BY A LEXA ; DATA OMITTED FOR HOSTS NOT PART OF THE T OP 10.        INLINE / INTERNAL / EXTERNAL SCRIPT & INCLUSIONS ( OF ANY TYPE ) DUE
                                                                             TO AD / TRACKER / WIDGET CODE . G REYED OUT IF <100 INCLUSIONS ;
        Hostname                          A LEXA    C OM                       OMITTED IF <10. L IBRARIES COUNTED AT MOST ONCE PER SITE .

        www.google.com                             11.5 %
        www.google-analytics.com           9.5 %    9.4 %                                         A LEXA                             C OM
        ak2.imgaft.com                              5.6 %                                 Included From      Ad/Tr./     Included From        Ad/Tr./
        ajax.googleapis.com                3.1 %    3.9 %                  Library      Inl.    Int.  Ext.   Widget    Inl.    Int.  Ext.     Widget
        pagead2.googlesyndication.com      2.9 %    1.5 %
        connect.facebook.net               2.8 %    1.5 %                  jQuery       2.8   58.6    38.7       4.1    2.8   29.0     68.1      25.4
        www.googletagmanager.com           2.4 %                           Modernizr    3.1   65.4    31.5      11.9    1.4   38.2     60.4       3.2
        www.googletagservices.com          2.1 %                           Bootstrap    1.8   70.4    27.7       0.3    0.5   49.6     49.8       3.3
        partner.googleadservices.com       2.0 %                           Yepnope      2.3   68.7    29.0      10.3    1.7   48.0     50.3       4.3
        www.googleadservices.com           2.0 %                           SWFObject    3.2   45.9    50.9      35.6    1.4   24.7     73.9       7.7
        platform.twitter.com               1.8 %    1.2 %                  Angular      2.4   56.5    41.1       3.9           6.0     93.9       5.2
        apis.google.com                    1.5 %    1.3 %                  YUI 3              45.6    54.2      20.4    6.6    1.9     91.5       2.3
        maps.googleapis.com                         1.3 %                  Ember       10.5   14.6    74.8      35.3                   95.7      37.3
        s.ytimg.com                                 1.2 %                  Dojo        33.3   52.9    13.8             40.5            52.7



   TABLE III.    D ETECTED JS LIBRARIES : T OP 10 MARKET SHARE .          of our 72 target libraries on 87.7 % of all A LEXA sites and
S ORTED BY A LEXA ; DATA OMITTED FOR HOSTS NOT PART OF THE T OP 10.
                                                                          46.5 % of all sites in C OM. Table I lists the 30 libraries that
        Hostname                          A LEXA    C OM                  appear on most sites in A LEXA. We also list the percentage
        ajax.googleapis.com               16.7 %   13.3 %
                                                                          of sites in C OM that include the same libraries; however, for
        code.jquery.com                    3.3 %    4.0 %                 clarity we omit three libraries (all below 2 %) that are part of
        static.hugedomains.com                      4.0 %                 the Top 30 in C OM but not in A LEXA. jQuery is by far the
        static.parastorage.com                      3.1 %
        img.sedoparking.com                         2.2 %                 most popular library, which we find on 84.5 % of the A LEXA
        cdnjs.cloudflare.com               2.0 %    1.3 %                 sites and 62.6 % of the C OM sites. SWFObject, a library used
        ak2.imgaft.com                              2.0 %                 to include Adobe Flash content, is ranked seventh (10.7 %) and
        img4.wsimg.com                              2.0 %
        static.squarespace.com                      1.9 %                 sixth (5.2 %) despite being discontinued [33] since 2013. On
        s0.2mdn.net                        1.4 %                          the other hand, several relatively well-known libraries such as
        ad.turn.com                        1.4 %
        ivid-cdn.adhigh.net                1.1 %
                                                                          D3, Dojo and Leaflet appear below the Top 30 in both crawls.
        cdn2.editmysite.com                         1.1 %                 We also observe that the market share for these libraries is
        maxcdn.bootstrapcdn.com            0.9 %                          generally consistent with the data reported by Wappalyzer.
        cdn10.trafficjunky.net             0.4 %
        netdna.bootstrapcdn.com            0.4 %
        ajax.aspnetcdn.com                 0.3 %                              Figures 4 and 7 show the distribution of detected libraries
                                                                          with respect to their inclusion type (i.e., inline, internal, and
                                                                          external) in the two crawls. We detect libraries in inline script
                                                                          on 4.9 % of all A LEXA sites (and 3.1 % of C OM sites), which
at least one external script, which is consistent with the 88.5 %         occurs when library code is copy-pasted into a <script> tag
Nikiforakis et al. found for the Alexa Top 10 k [22]. The                 directly embedded in HTML, or when a script is generated by
median number of internal scripts is 4 per site in A LEXA (0 in           evaluating a string. For instance, we observe that a version of
C OM) and the maximum is 224 (134). Externally-hosted script              Dojo loads additional components by making an asynchronous
appears more prevalent with a median of 9 in A LEXA (max:                 request and passing the response to eval(). We detect internal
202) and 2 in C OM (max: 172); about 5 % of sites in both                 and external inclusions in 63.2 % and 43.1 % of all sites with
crawls include more than 29 external scripts. However, we note            an overall median of two different libraries per site in A LEXA
that we likely underestimate the fraction of internal scripts             (C OM: internal libraries on 22 % of sites, external on 49.5 %,
because our domain name-based heuristic cannot infer that two             with a median of one library per site).
domains may be under the same administrative control when
one is not a subdomain of the other. In both crawls, the median               Table III contains the most frequent hosts serving detected
inclusion depth of an internal script node is 1, i.e., directly           JavaScript libraries. Unsurprisingly, JavaScript CDNs are well
included by the main document, whereas the median depth of                represented (five in the Top 10 of A LEXA and three in the
an external script node is 2, suggesting indirect inclusions.             Top 10 of C OM); they were all used to build our catalogue
                                                                          of library reference files. Between 13 % and 16 % of all non-
    Each site includes scripts from a median of 5 external hosts
                                                                          inline JavaScript library inclusions are loaded from Google’s
in A LEXA (2 in C OM) with a maximum of 50 (45). Table II
                                                                          CDN. Perhaps illustrating the “long tail” nature of the C OM
lists the hostnames most frequently included by <script>
                                                                          crawl, we observe that several web hosting and domain parking
tags (counting each hostname at most once per site to reduce
                                                                          companies are represented in the Top 10 library sources of
bias through multiple inclusions). At least 7 of the Top 10
                                                                          C OM (note that we did not group subdomains; e.g., there are
in A LEXA are related to advertising, especially Google’s ad
                                                                          multiple other subdomains of wsimg.com below the Top 10).
platforms. This agrees with prior work that has found Google’s
Web advertising presence to be nearly ubiquitous ([10], [7]).                 Table IV lists the percentage of inline, internal, and external
                                                                          inclusions for a selection of detected libraries. While the
C. General Library Statistics                                             majority of inclusions in A LEXA come from internally-hosted
                                                                          sources (i.e., the website’s own domain), most inclusions are
    Next, we narrow our analysis to focus just on JavaScript              external in C OM. About 10.2 % of all sites in A LEXA (16.6 %
libraries from our catalogue. Overall, we detect at least one             in C OM) contain at least one library inclusion that is causally

                                                                      9
                             0.9                                                                                                            3500




                                                                                                        Days behind newest release (max.)
                             0.8
                                                                                                                                            3000
                                                                   At least one library
Fraction of sites in Top x



                             0.7
                                                                   Vulnerable                                                               2500
                             0.6
                             0.5                                                                                                            2000

                             0.4                                                                                                            1500
                                           0.8
                             0.3
                                           0.6                                                                                              1000
                             0.2           0.4

                                           0.2                                                                                              500
                             0.1
                                           0.0
                                                 0     200   400       600     800    1000
                             0.0                                                                                                              0
                                   0   10000         20000    30000          40000        50000                                                    Governm.Financial Malicious Parked   Adult   Random
                                                       Top x sites
Fig. 9. Fraction of websites with detected vulnerable inclusions in the                                Fig. 10. Box plot of each website’s maximum lag behind the newest release
A LEXA Top x, and fraction of websites with at least one detected library for                          of each detected library, broken down into site categories and a random sample
comparison. Vulnerable libraries can be found on 26 % of Top 500 websites.                             of 10,000 websites (A LEXA). Governmental sites are longest behind.


related to ad, tracker or widget code; SWFObject, Ember, and                                           inclusions of known vulnerable versions can make up even a
YUI 3 are relatively often included by such code.                                                      majority of all inclusions of a library. However, we caution
                                                                                                       that these numbers are not suitable for comparing the relative
D. Vulnerable Libraries                                                                                vulnerabilities of different libraries (see Section III-A5).
    We now move on to answering our research questions,
starting with how many websites become potentially vulnerable                                          E. Risk Factors for Vulnerability
due to including a library version with known vulnerabilities.
    Figure 5 shows the distribution of total and vulnerable                                                So far, we have examined whether sites are potentially
libraries per A LEXA website. Overall, we find that 37.8 %                                             vulnerable, that is, whether they include one or more known
of sites use at least one library version that we know to be                                           vulnerable libraries. In the following, we turn to how libraries
vulnerable, and 9.7 % use two or more different vulnerable                                             are included by sites and whether we can identify any factors
library versions (C OM in Figure 8: 37.4 % and 4.1 %).                                                 indicating a higher fraction of vulnerable inclusions.

    To better understand how website popularity is correlated                                              When we look at how the library is being hosted, inline
with vulnerable libraries, we plot in Figure 9 the percentage                                          inclusions of jQuery have a clearly higher fraction of vulnerable
of vulnerable A LEXA websites according to their Alexa ranks.                                          versions than internally or externally hosted copies. The
Highly-ranked websites tend to be less likely to include                                               situation is similar when focusing on the parent node of a
vulnerable libraries, but they are also less likely to include                                         library, that is, the document or script that caused the library
any detected library at all. Towards the lower ranks, both                                             to be included. For most libraries, direct inclusions by the
curves increase at a similar pace until they stabilise. While                                          main document are less likely to be vulnerable than indirect
only 21 % of the Top 100 websites use a known vulnerable                                               inclusions such as through an intermediate script or in a frame.
library, this percentage increases to 32.2 % in the Top 1 k
before it stabilises in the Top 5 k and remains around the                                                 As an example for web applications, we briefly survey
overall average of 37.8 % for all 75 k websites. This indicates                                        library use related to WordPress. We consider a library to be
that even relatively large websites (albeit not the largest ones)                                      used by WordPress if the URL of either the library or its
have a vulnerability rate comparable to the average of the C OM                                        next parent in the causality tree contains /wp-content/;
dataset, which is dominated by smaller sites.                                                          an inclusion is unrelated if neither condition holds. For all
                                                                                                       libraries, WordPress-related inclusions appear to be slightly
    When grouping A LEXA according to the McAfee Smart-                                                more up-to-date than unrelated inclusions.
Filter web categorisation [19], we find that financial and
governmental websites rank last with 52 % and 50 % vulnerable                                              On the other hand, library inclusions by ad, widget or tracker
sites, respectively. Malicious websites (e.g., spam) have the                                          code appear to be more vulnerable than unrelated inclusions.
same proportion as the full dataset, while parked and adult                                            While the difference is relatively small for jQuery in A LEXA,
sites are the least vulnerable with 24 % and 19 %, respectively.                                       the vulnerability rate of jQuery associated with ad, widget
                                                                                                       or tracker code in C OM—89 %—is almost double the rate of
    Table V shows the percentage of vulnerable copies for 5 out                                        unrelated inclusions. We speculate that this may be due to the
of the 11 JavaScript libraries in our catalogue with vulnerability                                     use of less reputable ad networks or widgets on the smaller
data. In A LEXA, 36.7 % of jQuery inclusions are known                                                 sites in C OM as opposed to the larger sites in A LEXA.
vulnerable, when at most one inclusion of a specific library
version is counted per site. Angular has 40.1 % vulnerable                                                 We note that the factors above are relatively coarse and
inclusions, Handlebars has 86.6 %, and YUI 3 has 87.3 % (it                                            the effects are sometimes opposite for different libraries. We
is not maintained any more). These numbers illustrate that                                             suggest further investigation into the underlying causes.

                                                                                                  10
TABLE V.           V ULNERABLE FRACTION OF INCLUSIONS PER LIBRARY, COUNTING AT MOST ONE LIBRARY- VERSION PAIR PER SITE . C ONFIGURATIONS WITH
                      LESS THAN 100 VULNERABLE INCLUSIONS GRAYED OUT; OMITTED IF LESS THAN 10 TOTAL INCLUSIONS AFTER FILTER .


                                      (a) A LEXA                                                                                   (b) C OM
  Inclusion Filter         jQuery           jQ-UI        Angular Handlebars         YUI 3          Inclusion Filter       jQuery     jQ-UI    Angular Handlebars    YUI 3
  All Inclusions            36.7 %          33.7 %        40.1 %           86.6 %   87.3 %         All Inclusions         55.4 %    37.3 %     38.7 %     87.5 %    13.7 %
  Internal                  38.1 %          33.0 %        37.1 %           84.6 %   92.8 %         Internal               41.6 %    28.1 %     45.8 %    100.0 %    68.8 %
  External                  34.8 %          35.5 %        48.2 %           88.6 %   83.8 %         External               62.7 %    42.7 %     38.4 %     85.4 %    12.6 %
  Inline                    54.8 %          30.7 %        20.0 %          100.0 %         -        Inline                 89.9 %    25.6 %           -          -         -
  Internal Parent           37.1 %          33.7 %        40.6 %           85.3 %   91.3 %         Internal Parent        59.7 %    37.0 %     78.2 %     94.2 %    47.9 %
  External Parent           32.6 %          33.8 %        41.8 %           96.4 %   92.2 %         External Parent        45.9 %    38.0 %     20.7 %     84.1 %    68.9 %
  Inline Parent             47.6 %          35.2 %        25.6 %           83.7 %   79.6 %         Inline Parent          79.8 %    48.9 %           -          -    1.0 %
  Direct Incl. in Root      36.4 %          33.5 %        40.1 %           85.2 %   90.4 %         Direct Incl. in Root   42.6 %    36.2 %     41.3 %     88.7 %    50.4 %
  Indirect Inclusion        41.0 %          35.6 %        43.2 %           92.3 %   87.1 %         Indirect Inclusion     77.5 %    44.5 %     30.8 %     86.4 %     7.5 %
  WordPress                 29.2 %          14.4 %        23.5 %           77.3 %         -        WordPress              41.6 %    20.4 %     25.0 %     70.3 %          -
  Non-WordPress             36.8 %          34.2 %        40.7 %           86.8 %   87.3 %         Non-WordPress          55.6 %    38.6 %     38.9 %     90.7 %    13.7 %
  Ad/Widget/Tracker         38.1 %          39.8 %        23.5 %           96.9 %   98.2 %         Ad/Widget/Tracker      89.0 %    31.6 %     19.2 %           -   55.0 %
  No Ad/Widget/Tracker      36.7 %          33.6 %        40.8 %           86.5 %   84.1 %         No Ad/Widget/Tracker   45.5 %    37.3 %     39.6 %     87.3 %    12.7 %



TABLE VI.    DAYS BETWEEN RELEASE OF USED VERSION AND NEWEST
  AVAILABLE LIBRARY VERSION ( MEDIAN OVER INCLUSIONS ). S HOWN
                                                                                                   inclusion in A LEXA uses a version 491 days older than the
  SEPARATELY FOR VULNERABLE AND NON - VULNERABLE INCLUSIONS                                        newest D3 release. For Mootools, the lag is 1,417 days.
  WHERE AVAILABLE , OR ( CENTRED ) FOR ALL INCLUSIONS OTHERWISE .
     G REYED OUT WHEN LESS THAN 100 INCLUSIONS OBSERVED .                                              To characterise lag from a per-site point of view, we
                                                                                                   calculate the maximum lag of all inclusions on each site and find
                                       A LEXA                      C OM                            that 61.4 % of A LEXA sites are at least one patch version behind
              Library           Vuln.         Non-V.       Vuln.      Non-V.                       on one of their included libraries (C OM: 46.2 %). Similarly, the
              jQuery             1476              705     2243            705                     median A LEXA site uses a version released 1,177 days (C OM:
              jQuery-Migrate     1105             1024     1105           1024                     1,476 days) before the newest available release of the library.
              jQuery-Mobile                 49                     122
              Bootstrap                    304                     389                                 Finally, we plot this lag according to the category of A LEXA
              Angular                657           234      489            287
              Handlebars             687             0      687             57                     sites in Figure 10. Each candlestick shows the 5th , 25th , 50th ,
              Prototype                    1772                    1772                            75th , and 95th percentiles, respectively. We observe that both
              MooTools                     1417                    1417                            governmental and financial websites are the longest behind
              YUI 3                  393           140      393            140
              Raphael                      1491                    1491                            current releases with a median lag of 1,293 and 1,1239 days,
              D3                           491                     139                             respectively. Similar to per-category vulnerability rates, parked
                                                                                                   and adult websites exhibit values better than the average.
                                                                                                      In summary, these results demonstrate that the majority of
F. Relative Age of Libraries                                                                       web developers are working with library versions released a
    When websites include outdated libraries, an interesting                                       long time ago. We observe median lags measured in years,
question is how far they are behind more current versions of the                                   suggesting that web developers rarely update their library
libraries. We begin by looking at how far sites are behind the                                     dependencies once they have deployed a site.
most recent patch-level releases of libraries. Patch-level releases
are usually (though not always) backwards-compatible. In                                           G. Duplicate Inclusions
A LEXA, we observe that non-vulnerable inclusions of Angular
lag behind by a median of five versions, whereas the median                                            While analysing the use of JavaScript libraries on websites,
is seven versions for vulnerable inclusions.                                                       we noticed that libraries are often used in unexpected ways.
                                                                                                   We discuss some examples using jQuery as a case study. About
    When looking at the number of days between the release                                         20.7 % of the websites including jQuery in A LEXA (17.2 % in
date of the included version and the release date of the newest                                    C OM) do so two or more times. While it may be necessary to
available library version overall, the lag for Angular is 398 days                                 include a library multiple times within different documents from
for all inclusions, 657 days for vulnerable inclusions, and                                        different origins, 4.2 % of websites using jQuery in A LEXA
234 days for non-vulnerable inclusions. While this may suggest                                     include the same version of the library two or more times
that there is a relationship between higher lag and vulnerability                                  into the same document (5.1 % in C OM), and 10.9 % (5.7 %)
of a site, we note that the lag is also tied to the availability                                   include two or more different versions of jQuery into the
of newer versions—if no update is available, the lag is zero                                       same document. Since jQuery registers itself as a window-
yet the used version could be vulnerable. For instance, 6.7 %                                      global variable, unless special steps are taken only the last
of all Angular inclusions in A LEXA use that latest available                                      loaded and executed instance can be used by client code. For
release in their respective patch branch yet remain vulnerable                                     asynchronously included instances, it may even be difficult to
because the branch contains no fixed version.                                                      predict which version will prevail in the end.
   With this caveat in mind, Table VI shows the median lag (in                                         Figure 11 shows the causality tree of ms.gov, the site with
days) behind the most recently released version of each library.                                   the highest number of identical jQuery inclusions in a single
We observe significant variations. For instance, the median D3                                     document. Only one instance (version 2.2.2) is included

                                                                                              11
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Fig. 11. Causality tree for ms.gov, showing multiple jQuery inclusions in        Fig. 12. Causality tree for mercantil.com with multiple jQuery inclusions,
a single document. One instance is referenced in the main HTML page; all         two libraries concatenated into one file (Modernizr and Yepnope), and complex
others are included transitively by other scripts.                               element relationships in social media widgets (Twitter, Facebook and LinkedIn).


directly in the source code of the main HTML page; all twelve                    with a more recent major or minor version, which might
other jQuery inclusions (of version 2.2.0) are injected by                       necessitate additional code changes due to incompatibilities.
various self-hosted scripts in quick succession.
                                                                                 Version Aliasing.      Some JavaScript CDNs support version
    In contrast, the inclusions of four different jQuery versions                aliasing, where the developer may specify only a prefix of
on mercantil.com (Figure 12) are all referenced directly in                      the requested library version and the CDN will automatically
the main page’s source code, some of them directly adjacent to                   return the latest available version with that prefix. In theory,
each other. While we can only speculate why these cases occur,                   version aliasing appears to be a robust strategy for developers
at least some of them may be related to server-side templating,                  to easily keep their library dependencies up-to-date. We scan
or the combination of independently developed components                         our crawl for library inclusions with URLs of a CDN, and
into a single document. Indeed, we have observed cases where                     detect version aliasing whenever (1) the version given in the
a web application (e.g., a WordPress plug-in) that bundled                       URL has only one or two components, such as 1.2, and (2)
its own version of a library was integrated into a page that                     the library version detected by the static or dynamic method
already contained a separate copy of the same library. Since                     is greater than the prefix extended with zeros, such as 1.2.3
duplicate inclusions of a library do not necessarily break any                   instead of 1.2.0. In A LEXA (and C OM), we detect 1,489
functionality, we suspect that many web developers may not                       (914) confirmed instances of version aliasing, counting at most
be aware that they include a library multiple times, and even                    one per library on each site. Overall, however, the frequency of
less that the duplicate inclusion may be potentially vulnerable.                 version aliasing is very small—only around 1.1 % of all sites
                                                                                 that include jQuery use version aliasing.
H. Remediations
                                                                                     Except for one, all instances of aliasing refer to Google’s
    From a remediation perspective, the picture painted by                       CDN, with jQuery being the most frequent library. One one
our data is bleak. We observe that only very small fraction                      hand, 47.2 % (37.9 %) of jQuery inclusions with aliasing are
of potentially vulnerable sites (2.8 % in A LEXA, 1.6 % in                       avoiding a vulnerability, i.e., the inclusions point to a branch
C OM) could become free of vulnerabilities by applying patch-                    that has a known vulnerability, but the issue is addressed by the
level updates, i.e., an update of the least significant version                  latest version. On the other hand, while version aliasing may
component, such as from 1.2.3 to 1.2.4, which would                              seem like a good way to automatically avoid vulnerabilities,
generally be expected to be backwards compatible. The vast                       Google recently discontinued this service, citing caching issues
majority of sites would need to install at least one library                     and “lack of compatibility between even minor versions” [11].

                                                                            12
                       V.    D ISCUSSION                                   or social media widget code have a higher rate of vulnerability
                                                                           than other inclusions. Such components are often hosted on
    Our research has shown that even though patches may be                 third-party servers and loaded dynamically through client-
available, vulnerable JavaScript libraries are in widespread use           side JavaScript. Additional libraries loaded at runtime by
on the Web. In the following, we discuss approaches that we                these components do not appear in the website’s codebase,
believe could improve the situation.                                       and web developers may be unaware that they are indirectly
    Dependency Management: Before website developers can                   including vulnerable code into their website. Similarly, dynamic
update potentially vulnerable libraries that they are using, they          inclusions of libraries by third-party components may explain
must be aware of which libraries they are using. Instead of                some of the same-document duplicate inclusions that we noticed.
manually copying library files or CDN links into their codebase,           In addition to keeping their library dependencies up to date,
developers should consider more systematic approaches to                   developers of web services meant to be included into other
dependency management. Bower [6] and the more server-                      websites could avoid replacing an existing library instance
oriented Node Package Manager [23] allow developers to                     by using a methodology similar to ours to test whether the
declare external dependencies in a configuration file and can              library has already been loaded into the page before adding
automatically download and include the code into the project.              their own copy. On the other hand, web developers who intend
Tools such as Auditjs [2] (for Node projects) scan dependencies            to use third-party components such as advertising code for their
for known vulnerabilities and can be integrated into automated             website can attempt to limit potential damage by isolating these
build processes; such solutions, however, work only if the                 components in separate frames whenever this is feasible.
developer has an understanding of the risks associated with
using vulnerable libraries, and is aware of the audit tool itself.
Therefore, this functionality would ideally be integrated into the                              VI.   R ELATED W ORK
dependency management system of the programming platform                       Our work is related to prior studies on JavaScript security,
so that a warning can be shown each time a developer includes              measurements of vulnerability patching and dependency man-
a known vulnerable component from the central repository.                  agement, a series of blog posts about inclusions of vulnerable
    Code Maintenance: Effective strategies to have web devel-              libraries that inspired our more in-depth analysis, and existing
opers update vulnerable libraries work only when vulnerability             tools that implement a subset of our detection methodology.
information is properly tracked and disseminated. Unfortunately,
                                                                               JavaScript Security: In [22], Nikiforakis et al. identify
security does not appear to be a priority in the JavaScript library
                                                                           the network sources of JavaScript inclusions in the Alexa
ecosystem. Popular vulnerability databases contain nearly no
                                                                           Top 10 k websites, without special consideration for libraries
entries regarding JavaScript libraries. During this entire work,
                                                                           or corresponding versioning semantics, and develop host-based
we did not encounter a single popular library that had a
                                                                           metrics for maintenance quality to assess whether remote code
dedicated mailing list for security announcements (in fact,
                                                                           providers could be compromised by attackers and subsequently
most libraries we investigated did not have a mailing list for
                                                                           serve malicious JavaScript. Whereas Nikiforakis et al. study
announcements at all). Furthermore, only a few JavaScript
                                                                           where included code is hosted, we focus on the narrower but
library developers provide a dedicated email address where
                                                                           semantically richer setting of libraries to investigate whether
users can submit vulnerability reports. When the release notes
                                                                           included code is outdated or known to be vulnerable, and we
of libraries mention at all that a vulnerability has been fixed,
                                                                           leverage our deep browser instrumentation to determine the
they often do not provide any details about the affected code, or
                                                                           initiators and causes of such inclusions.
which prior versions are vulnerable. This is problematic because
web developers using that library do not know whether the                      A separate class of related work examines specific at-
vulnerable code is a function that they depend on, and whether             tack vectors in client-side JavaScript and conducts crawls
an update is required. Libraries can even silently reintroduce             to estimate how many websites are subject to the attack:
vulnerabilities in order to remain backwards-compatible [12].              Lekies and Johns [16] survey insecure usage of JavaScript’s
Although jQuery is an immensely popular library, the fact                  localStorage() function for code caching purposes, Son
that searching for “security” or “vulnerability” in the official           and Shmatikov [31] examine vulnerabilities arising from
learning centre returns “Apologies, but nothing matched your               unsafe uses of the postMessage() function, Lekies et
search criteria” is an excellent summary of the state of                   al. [17] detect and validate DOM-based XSS vulnerabilities,
JavaScript library security on the Internet, circa August 2016.            and Richards et al. [29] analyse websites’ usage patterns
A similar lack of adequate information about security issues               of the problematic eval() API. Yue and Wang [39] study
has also been reported for the Android library ecosystem [3].              several insecure practices related to JavaScript, namely cross-
    An additional complication is that patches are often supplied          domain inclusion of scripts as well as the execution and
only for the most recent versions of a library. Yet, these versions        rendering of dynamically generated JavaScript and HTML
are not necessarily backwards-compatible with the versions still           through eval() and document.write(), respectively. Li
in use by many web developers. In fact, the short development              et al. [18] detect malicious redirection code hidden in JavaScript
lifecycle common in web development can become a burden                    files on compromised hosts by deriving signatures from the
for developers who need to keep up with frequent breaking                  differences between infected library files and the original,
changes in libraries in order to maintain their websites up to             benign copies. In contrast to the above work, we do not focus
date and free of vulnerable libraries.                                     on specific vulnerabilities, the use of security-critical functions,
                                                                           or malicious files. Instead, we provide empirical results at a
    Third-Party Components: We observed that libraries in-                 more abstract level to highlight and explain the prevalence of
cluded by third-party components such as advertising, tracking             benign-but-vulnerable JavaScript libraries in the wild.

                                                                      13
    Vulnerability and Dependency Management: Four stud-                   websites, uncovering issues such as duplicate library inclusions
ies have examined vulnerability patching and dependency                   as well as transitive (and on average more vulnerable) inclusions
management in large software ecosystems, although not with                of libraries by third-party modules such as advertising, tracking,
respect to JavaScript or the Web. Sonatype Inc., the company              and social media widget code.
behind Maven, released a report [32] examining security
maintenance practices observed from the vantage point of                     Tools: From the point of view of our library detection
the largest repository of Java components. According to the               methodology, we are aware of two open source tools with a
report, the mean time-to-repair of a security vulnerability in            similar approach: Retire.js and the Library Detector extension.
component dependencies is 390 days, 51,000 of the components              Library Detector Chrome Extension. This browser exten-
in the repository have known security concerns, and 6.2 % of              sion [20] aims to detect the JavaScript libraries running on a
downloaded components include known vulnerabilities. A key                website. It injects a script into the website’s main document
observation in the report is that fixing serious flaws in open            to test for the presence of known libraries and extracts their
source code does not stop vulnerable versions from being used.            version, using dynamic detection code similar to the approach
Our work in this paper shows that there are similar trends with           presented in Section III-B. The extension does not warn against
respect to JavaScript library usage on the Web.                           known vulnerabilities, does not reveal how or why a library
    Nappa et al. analyse the patch deployment process for                 was included, cannot reliably detect duplicate inclusions, and
1,593 vulnerabilities in 10 applications installed on 8.4 million         does not analyse libraries loaded in frames.
Windows hosts worldwide [21]. The authors show that the time
                                                                          Retire.js. Along with his blog posts, Oftedal released a tool [27]
until a patch is released for different applications affected by
                                                                          to help web developers detect JavaScript libraries with known
the same vulnerability in a shared library can differ by up to
                                                                          vulnerabilities. In a nutshell, Retire.js is a browser extension
118 days, with a median of 11 days. Furthermore, patching
                                                                          that intercepts network requests for JavaScript files while
rates vary among applications and depend, among other factors,
                                                                          a website is loading and detects libraries based on known
on the update mechanism. At most 14 % of vulnerable hosts
                                                                          file hashes, regular expressions over the file contents, and
are patched before an exploit is released.
                                                                          API method signatures dynamically evaluated in an empty
    Thomas et al. propose an exponential decay model to                   sandbox environment. While we also use dynamic detection
estimate patching delays of Android devices [35]. According               and hash detection approaches in our methodology, Retire.js
to the model, when a new version of the operating system is               makes several simplifications that limit the tool’s utility for
released, it takes 3.4 years to reach 95 % of the devices.                our analysis. First, detecting a script as a library in an empty
                                                                          sandbox fails when the library has unmet dependencies. jQuery-
     Backes et al. build L IB S COUT [3], a system to detect
                                                                          UI, for instance, requires jQuery and hence cannot be detected
third-party library code in Android applications using a static
                                                                          dynamically if jQuery is not present in the environment. Second,
approach based on abstracted package trees and method
                                                                          intercepting requests only at the network level may miss inline
signatures. They find that 70.4 % of library inclusions in their
                                                                          scripts, dynamically evaluated scripts, and duplicate inclusions
dataset include an outdated version, and it takes developers
                                                                          of cached scripts. Most importantly, Retire.js does not reveal
an average of almost one year to migrate their applications to
                                                                          why a library was included, that is, whether the inclusion was
a newer library version after the library has been updated. In
                                                                          caused by advertising code, for instance. We support all of
a case study of two vulnerabilities, the authors show that the
                                                                          these scenarios and found interesting results as a consequence,
average update delay is 59 and 188 days after the library patch is
                                                                          such as the vulnerability rates per inclusion type in Table V,
first made available, while some applications remain without any
                                                                          and the duplicate inclusions observed in Section IV-G.
update. Furthermore, 10 out of 39 advertising libraries contain
one or more versions that improperly use cryptographic APIs.
In contrast to L IB S COUT, our detection approach requires that                               VII.    C ONCLUSION
the library API methods used in our signatures not be renamed
or removed. While a theoretical possibility, we believe that such             Third-party JavaScript libraries such as Angular, Bootstrap
eager minification settings are exceedingly rare on the Web               and jQuery are are frequently used on websites today. While
since they would necessitate processing all code potentially              such libraries allow web developers to create highly interactive,
referencing the library, including in HTML attributes and inline          visually appealing websites, vulnerabilities in these libraries
script. To the best of our knowledge, the default settings of             might increase the attack surface of the websites that depend
minifiers typically do not rename methods or remove dead                  on them. Hence, it is very important to ensure that only recent,
code in client-side JavaScript (see, for instance, the Closure            patched versions of these libraries are being utilised.
Compiler [9]). This assumption allows us to detect the version                In this paper, we presented the first comprehensive study on
of a JavaScript library more reliably since most libraries self-          the security implications surrounding JavaScript library usage
identify via their version attribute or method.                           on real-world websites. We found that:
    Blog Posts: In 2014, a series of blog posts by Oftedal ([24],
[25], [26]) raised awareness about the use of outdated JavaScript            •    87.7 % of A LEXA Top 75 k websites and 46.5 % of
libraries on the Web, and the fact that many large companies                      C OM websites use at least one of the 72 JavaScript
(including banks) use versions that are known to be vulnerable.                   libraries in our catalogue (Section IV-C);
We complement this first exploration of the issue with a more                •    more than 37 % of websites use at least one library
comprehensive detection methodology and a more detailed                           version with a known vulnerability, and vulnerable
analysis. To the best of our knowledge, we are the first to report                inclusions can account for a significant portion of all
on the modality and causes of JavaScript library inclusions in                    observed inclusions of a library (Section IV-D);

                                                                     14
    •     the median lag between the oldest library version used                      [12] M. Heiderich, “jQuery Migrate is a Sink, too?! or How jQuery Migrate
          on each website and the newest available version of                              un-fixes a nasty DOMXSS without telling us.” April 2013, http://blog.
          that library is 1,177 days in A LEXA and 1,476 days                              mindedsecurity.com/2013/04/jquery-migrate-is-sink-too.html.
          in C OM (Section IV-F), and development of some                             [13] jQuery, “Write less, do more.” 2016, https://jquery.com/.
                                                                                      [14] “#9521 (XSS with $(location.hash) and $(#<tag>) is needed?) – jQuery
          libraries still in active use ceased years ago;                                  – Bug Tracker,” https://bugs.jquery.com/ticket/9521, June 2011.
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The causality trees shown in this work can be viewed online:                               CCS, 2012.
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                       ACKNOWLEDGEMENTS                                               [25] E. Oftedal, “Scanning Fortune 500 for JavaScript libraries with known
                                                                                           vulerabilities,” February 2014, http://erlend.oftedal.no/blog/?blogid=143.
    This work was supported by the National Science Founda-                           [26] E. Oftedal, “Scanning Norway for JavaScript libraries,” January 2014,
tion under grants CNS-1409738 and CNS-1563320.                                             http://erlend.oftedal.no/blog/?blogid=140.
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