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# Talking About My Generation: Targeted DOM-based XSS Exploit Generation using Dynamic Data Flow Analysis

**Talking About My Generation: Targeted DOM-based XSS Exploit Generation using Dynamic Data Flow Analysis** - Souphiane Bensalim, David Klein, Thomas Barber, Martin Johns, ACM.

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--- page 1 ---

Talking About My Generation: Targeted DOM-based XSS Exploit
Generation using Dynamic Data Flow Analysis
Souphiane Bensalim
SAP Security Research
souphianebensalim@gmail.com
David Klein
Technische Universität Braunschweig
david.klein@tu-braunschweig.de
Thomas Barber
SAP Security Research
thomas.barber@sap.com
Martin Johns
Technische Universität Braunschweig
m.johns@tu-braunschweig.de
ABSTRACT
Since the invention of JavaScript 25 years ago, website function-
ality has been continuously shifting from the server-side to the
client-side. Web browsers have evolved into an application plat-
form, and HTML5 emerged as a first-class environment for building
rich cross-platform applications. This additional functionality on
the client-side comes with the added risk of new security issues
with increasingly severe consequences. In this work, we investigate
the prevalence of DOM-based Cross-Site Scripting (DOM-based
XSS) in the top 100,000 most popular websites using a novel tar-
geted exploit generation technique based on dynamic data-flow
tracking. In total, this work finds 15,710 potentially insecure data-
flows where information from the URL is injected into the HTML of
the Web page. Using large-scale exploit generation and validation
services, 7199 of these flows lead to JavaScript execution, across
711 different domains. This represents a successful exploit rate of
45.82%, improving on previous methods by factors of 1.8 and 1.9
respectively.
CCS CONCEPTS
• Security and privacy →Browser security.
KEYWORDS
Web Security, DOM-based XSS, Exploit Generation, Taint Tracking
ACM Reference Format:
Souphiane Bensalim, David Klein, Thomas Barber, and Martin Johns. 2021.
Talking About My Generation: Targeted DOM-based XSS Exploit Gener-
ation using Dynamic Data Flow Analysis. In 14th European Workshop on
Systems Security (EuroSec’21), April 26, 2021, Online, United Kingdom. ACM,
New York, NY, USA, 7 pages. https://doi.org/10.1145/3447852.3458718
1
INTRODUCTION
The advent of Web 2.0 marked a new era of dynamic Web ap-
plications, leading to a shift from a static to an interactive Web.
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must be honored. Abstracting with credit is permitted. To copy otherwise, or republish,
to post on servers or to redistribute to lists, requires prior specific permission and/or a
fee. Request permissions from permissions@acm.org.
EuroSec’21, April 26, 2021, Online, United Kingdom
© 2021 Association for Computing Machinery.
ACM ISBN 978-1-4503-8337-0/21/04...$15.00
https://doi.org/10.1145/3447852.3458718
Client-side code gained more importance and grew in complex-
ity, with Web browsers becoming an application platform which
executes JavaScript code dynamically while documents are being
displayed. This change in the architecture of Web applications has
brought with it severe security implications on the client-side. As a
consequence, the number of security vulnerabilities in Web applica-
tions has increased significantly in parallel to the evolution of Web
technologies [1]. An example of the security issues that arose with
the new capabilities of Web 2.0 is related to the <iframe> tag used
to embed another document within the current document. This tag
has been shown to have several information leakage issues across
nested browsing contexts, which have lead to the introduction of
several security policies [13]. The best known of these policies is
the Same-Origin Policy (SOP) that defines resource access controls
in Web applications: A resource can access another resource only if
the two share the same origin [20].
To bypass these constraints, malicious attackers found new ways to
execute malicious code in the origin of vulnerable webpages, e.g.,
via so-called Cross-Site Scripting (XSS). This type of code injection
attack is used by an attacker to interact with the vulnerable Web
application in the name of the victim, with the main aim of stealing
sensitive session data and cookies. Until 2005, there were only two
kinds of Cross-Site Scripting: stored-XSS and reflected-XSS. By
2005, and due to the shift of Web applications to the client-side,
Klein [7] introduced DOM-based XSS as the third kind of Cross-Site
Scripting, which occurs purely inside client-side code.
Methods to detect and validate DOM-based XSS were first intro-
duced by Lekies et al. [8] and later improved by Melicher et al. [9].
Their techniques for URL exploit injection, are based on heuristic as-
sumptions about how URLs are processed by client-side JavaScript
code. In this paper, we aim to improve on these techniques by intro-
ducing a targeted exploit generation technique to ensure payloads
are injected into a URL in the position where they have the high-
est chance of triggering JavaScript execution. To achieve this goal,
we collected potentially vulnerable data-flows from the 100,000
top ranked domains according to the Tranco [12] list. Next, we
generated exploits using our novel exploit generation technique
and validated these exploits. To assure a fair comparison, we also
implemented the approaches from the past works and generated
exploits for the same collection of domains.
This paper is organized as follows: First DOM-based XSS is briefly
presented in Section 2 followed by an overview of the approach
taken in Section 3. Existing techniques for exploit generation are
presented in Section 4, succeeded by the introduction of a novel


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EuroSec’21, April 26, 2021, Online, United Kingdom
Souphiane Bensalim, David Klein, Thomas Barber, and Martin Johns
targeted exploit method in Section 5. Results are presented and
discussed in Section 6, followed by related work in Section 8 and
conclusions in Section 9.
2
BACKGROUND
Cross-Site Scripting is a type of security vulnerability in which
malicious scripts are injected into a Web application in order to
transfer sensitive data to a malicious third party [19]. Programming
errors in trusted websites are exploited to perform a script execution
within the Web browser of the victim, allowing the attacker to
perform various nefarious actions, including stealing cookies and
sensitive information, keylogging, session hijacking and so on.
In this section, we provide a brief summary of DOM-based XSS,
followed by a classification of the different contexts which can lead
to the execution of malicious scripts in client-side code.
2.1
DOM-based XSS
DOM-based Cross-Site Scripting is a special case of XSS which
appeared as Web applications have transitioned from traditional
static webpages to feature-rich client-side JavaScript rendering in
the Web browser. In DOM-based XSS, the script execution occurs
only while a webpage is being loaded or after it is loaded, which
means that the malicious script is not inserted into the webpage on
the server side but on the client-side. Here, the Web browser does
not differentiate between a safe script issued from the developer and
a malicious script injected by an attacker. In order to successfully
exploit this type of vulnerability, an attacker builds a crafted URL
containing the payload and delivers it to a victim e.g., via e-mail.
DOM-based XSS is caused by insecure data-flows present in client-
side code in which data from user-controlled source functions flows
into functions which allow script execution in the DOM (known
as sink functions). Examples of relevant sources for this work are
location and document.URL properties. Corresponding examples
for sink functions and properties that allow the programmers to
dynamically change the DOM (and therefore allow an attacker to
inject malicious scripts) are: document.write, innerHTML, eval
and attribute event handlers. In the following, we describe the
various sink function categories in more detail.
2.2
Script Execution Contexts
Determining the script execution context is crucial for effective
exploit generation, as each context has different exploitation criteria.
Specifically, we divide the execution contexts into five categories
as follows:
(1) HTML Context: It is possible for Web applications to inject
HTML code directly into the document of the page. In case the
injected code originates from a user-controlled source, DOM-
based XSS is possible. Within the HTML context we differen-
tiate between the injection into the HTML content and the
injection into the HTML attributes as follows:
• HTML content: JavaScript allows the dynamic creation and
modification of DOM Elements on a webpage. JavaScript can
be embedded directly in some methods, such as document.write
and document.writeln, by simply adding a <script> tag.
Other sinks, such as innerHTML and insertAdjacentHTML
insert, but do not execute the script, but can still be exploited
using Event Handlers, as described in Section 4.2.
• HTML attributes: An HTML attribute is set and modified
via JavaScript in two manners: Either by using the method
elem.setAttribute(name, value) or by using string con-
catenation. This makes a flow from a user-controllable source
to an attribute value possible as follows:
– If the attribute is an HTML5 Event Handler, href or src at-
tribute: The code can be injected directly into the attribute
and will be executed when the event occurs.
– Otherwise: The context should be first closed in order to
inject either an Event Handler inside the same tag or reach
an HTML Content context and inject a script.
The attribute context is divided into three sub-contexts: Sin-
gle Quoted attributes, Double Quoted attributes and Un-
quoted attributes.
(2) URL Context: In the URL-Context, a user-controlled input is
injected into a URL attribute of certain DOM elements such
as the href attribute of an anchor tag, the src attribute of an
image, iframe or embed tag, and the data attribute of the object
tag. Even if HTML encoding prevents breaking out of the HTML
context, there is still a potential threat. The attacker can, for
example, make use of the javascript:, data: or vbscript:
scheme to run the malicious script if they are able to control
the whole attribute.
(3) JavaScript Context: Web applications which turn user pro-
vided input into executable code, e.g., via eval(), Function(),
setTimeout(), or setInterval() are also at risk of DOM-
based XSS. These functions take the code in string format and
execute it as JavaScript. Template literals are another type of
JavaScript context introduced in ECMAScript6, consisting of
string literals encapsulated in backticks. Template literals allow
embedded JavaScript expressions using the ${...} syntax to
be evaluated and inserted in the DOM.
(4) JSON Context: In the JSON Context, a user-controllable in-
put is reflected as a JavaScript Object Notation (JSON) value.
JSON is a standard format used to represent structured data
as attribute-value pairs and array data types. It is commonly
used to serialize and transmit data within and between Web
applications. This context can be exploited by breaking out
from the JSON context into the JavaScript context and then
injecting malicious code.
(5) CSS Context: Cascading Style Sheets (CSS) provide a mech-
anism to add style to Web applications. In the CSS Context,
a user-controllable input is reflected into the value of a style
attribute or tag. This context is only relevant for XSS exploits
in legacy browsers, e.g., Internet Explorer, where JavaScript is
allowed to be part of the CSS value.
The contexts presented above are classified into three sink cate-
gories: HTML element sink (consisting of Items 1 and 2), execution
sink (Items 3 and 4) and CSS sink (Item 5). As described above, CSS
sinks are considered out of scope for this work.
3
OVERALL APPROACH
In this section, we describe an automated system for the detection
of potentially insecure data-flows in Web applications and how


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Talking About My Generation: Targeted DOM-based XSS Exploit Generation using Dynamic Data Flow Analysis
EuroSec’21, April 26, 2021, Online, United Kingdom
Web-Crawler
Server
Domains
Taint-Aware 
Browser
Crawling Worker
Taint-Notifier
Taint-Aware 
Browser
Crawling Worker
Taint-Notifier
Taint-Aware 
Browser
Crawling Worker
Taint-Notifier
Crawling
Generation
Server
Validation
Server
Selenium
Selenium
Selenium
Findings
Figure 1: Overview of the crawling, exploit generation and
validation architecture
they can be used for the generation and validation of DOM-based
XSS exploits. The approach is summarized in Figure 1 and consists
of three phases:
(1) Detection: The crawler visits specific web pages to collect
potentially insecure data-flows.
(2) Generation: Exploit URLs are generated from the appropriate
data-flows which could lead to a DOM-based XSS exploit.
(3) Validation: Generated URLs are tested to see whether they
lead to arbitrary JavaScript execution.
More details on the detection and validation phases is given in Sec-
tion 3.1 and Section 3.2 respectively. As exploit generation forms
the focus of this work, complete sections are dedicated to the de-
scriptions of existing techniques (Section 4) and a novel targeted
injection method (Section 5).
3.1
Detection of Insecure Flows
In order to detect insecure DOM-based XSS data flows from sources
to sinks, we employed a dynamic taint-tracking approach by directly
instrumenting the Web browser. To build this browser, we used the
open-source Firefox browser and enhanced its JavaScript engine
SpiderMonkey as well as the DOM implementation with functions
that allow the taint-tracking of all the strings present in a web page.
Every strings emanating from a source function is tainted by attach-
ing rich character-level metadata describing the sink function and
its location in the code. Any string transformation operations, such
as substring, concat and replace are also enhanced so that the
taint metadata is correctly propagated. In addition, a record of each
string operation and its code location is attached to the metadata,
allowing full reconstruction of the taint operations performed. If a
string with one or more tainted characters is detected entering a
sink function, a JavaScript event is triggered in the browser, which
is forwarded to an external database by a dedicated web extension.
For our study we considered the same set of sources and sinks as
the previous DOM-based XSS studies [8, 9].
The large-scale collection of taint flows is automated by embedding
our taint-aware browser in a Selenium-driven Docker container. A
central Web crawling service takes an input of seed domains which
are distributed to a scalable number of workers. Each worker will
load its designated URL before extracting all links present on the
page, which are sent back to the server and the worker is ready
to receive the next URL. Each worker waits for 10 seconds after
a successful page load in order to maximize the number of taint
flows collected. If an error occurs during loading, or the page is not
loaded after 30 seconds, an error or timeout status is returned to
the server respectively. The crawling server can be configured to
visit a fixed number of links per page, up to a certain depth from
the starting domain.
3.2
Exploit Validation
In order to explore the effectiveness of the exploits generated in
Section 4.2, we check whether they lead to JavaScript execution in
the validation stage. Validation is performed by a headless Firefox
browser, driven by Selenium and running inside a Docker container.
Each validation instance retrieves an unvalidated exploit URL from
the database, with a dedicated listener installed to detect execution
of the payload function. If the function is called, then the exploit
is considered successful and the result written back to storage.
Following a similar approach to Lekies et al. [8], we use a modified
Firefox browser which does not automatically encode the search
and fragment portions of the URL, in order to mimic the behavior
of legacy browsers such as Internet Explorer. In addition, each
exploit is opened in a fresh browser instance to minimize effects
due to, e.g., cookie storage.
4
ESTABLISHED EXPLOIT GENERATION
Existing methods for DOM-based XSS exploit generation are pre-
sented in this section, including an overview of exploit generation
(Section 4.1), followed by URL injection techniques (Section 4.2).
4.1
Context-sensitive Exploit Generation
In order to exploit a DOM-based XSS vulnerability, an attacker has
to build a URL containing a crafted malicious exploit. In general,
an XSS exploit consists of three parts as follows:
𝑒𝑥𝑝𝑙𝑜𝑖𝑡:= breakOut + payload + breakIn
The first part is the breakOut sequence whose purpose is to “break
out” of a non-executable context to a context where JavaScript can
be executed. The second part is the payload consisting of the script
code that has to be executed. The third part is the breakIn sequence
which serves to escape any subsequent code sequences in order to
prevent them from causing execution errors.
The break out sequence is generated following a similar method to
Lekies et al. [8], using a parser to identify the context (as described
in Section 2.2) where the tainted part has been injected and there-
fore generate context-dependent closing characters. For HTML
element sinks, some tags such as <textarea> prevent a script exe-
cution inside them. As the string flowing into a sink function will
typically only contain a fraction of the entire HTML code, we can-
not determine whether such a tag has previously been opened. In
order to counteract this effect, the breakOut sequence is appended
with a list of closing tags as follows:
</iframe></style></script></object></embed></textarea>
This is only possible due to the fault tolerance of the HTML parser,
which allows the presence of closing tags even without a preceding
opening tag.


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EuroSec’21, April 26, 2021, Online, United Kingdom
Souphiane Bensalim, David Klein, Thomas Barber, and Martin Johns
More care needs to be taken with the JavaScript parser, which blocks
code execution if a syntax error is detected. Therefore, the generated
exploit should replace the tainted portion of the string without
causing any syntax errors (for example by forgetting to close a
bracket or closing an unopened context). To minimize these errors,
we enhanced the parsing step with an error-correcting syntax check
to ensure execution will not be canceled.
After breaking out of the sink context to a context where a script
can be executed, the next step is to construct the target payload.
For this study, the payload is simply a custom function call, whose
execution is monitored as part of the validation stage. While an
execution sink represents a script context and therefore a direct call
to the function is possible, the HTML element sink will interpret
this call as text and will not execute it directly. In this case it is
necessary to first enter a script context as follows:
<script>reportingFunction()</script>
This technique succeeds for methods such as document.write and
document.writeln. However, HTML5 does not allow direct script
execution using the following properties: innerHTML, outerHTML
and insertAdjacentHTML. For these cases script execution is still
possible via an event handler as follows:
<img src=x onerror="reportingFunction()">
In this case, the image source x is deliberately unavailable, trigger-
ing execution of the onerror event handler.
Finally, the breakIn sequence serves to close the execution context
of the payload and then comment out any characters proceeding it.
This ensures that the parser will not abort the script execution due
to a syntax error.
Listing 1: location.hash flows to the JavaScript context
1
let hash = location.hash; // source
2
if (hash.length > 1) {
3
let username = unescape(hash.substr (1));
4
let editUsername = "<input type="text" value= \""
+ username + "\" />";
5
document.getElementById("msgboard").innerHTML =
editUsername; // sink
6
}
Listing 2: context-specific generated exploit
1
"><img src=x onerror="reportingFunction()"><!--
Listing 1 presents an example where data flows from location.hash
into innerHTML. The context is the HTML element sink, or more
precisely the HTML attribute context. A valid exploit generated by
the method described above is presented in Listing 2, where "> is
the break out sequence that closes the attribute context, followed by
an appropriate innerHTML payload, with <!-- providing the break
in sequence.
4.2
URL Injection Techniques
Once an exploit was created, it must be inserted into the URL in a
position which is most likely to result in the payload’s execution.
In this Section, we present two previous techniques which address
this issue, and present a novel method for targeted exploit injection.
Lekies et al. [8] proposed a method in 2013 (termed Method A in
the following) that consists of appending the URL with a fragment
(or hash) containing the generated exploit. As the fragment portion
of the URL is not sent as part of the HTTP request, this method has
the advantage of assuring that the server will not know that a URL
with a malicious script has been requested. In 2018, Melicher et
al. [9] presented a complementary approach (termed method B) that
treats query parameters as a special case, making it more specific
than method A. In their work, methods A and B were subsequently
combined to achieve a larger number of exploit candidates. Method
B consists of moving the query string in question to the fragment
and change its value to an exploit.
To highlight how methods A and B work, we provide an example
of a URL where a vulnerable flow has been identified coming from
the location.search source:
http://example.com/1?payload=abcd&sp=x
(1)
With method A, the exact location of the tainted string abcd within
the URL is not considered and a context-sensitive generated exploit
is independently appended as follows:
Method A:
http://example.com/1?payload=abcd&sp=x#EXPLOIT
In comparison, method B determines whether the tainted string
appears within a query parameter, as is the case in the example
above. Method B will then extract the corresponding key-value pair
from the query and replace the complete value with the generated
exploit, for example as follows:
Method B: http://example.com/1?sp=x#&payload=EXPLOIT
Based on the results of [9], URL parameters are commonly extracted
in client-side JavaScript code by searching for characters like ?, &
and =. For this reason, a & character is added to the extracted key-
value pair at the beginning of the exploit.
5
TARGETED EXPLOIT GENERATION
Both of the existing methods, however, are based on heuristic as-
sumptions about how the URL is processed by client-side code, and
thus suffer from a level of imprecision. To address this issue a novel,
targeted exploit injection technique (termed Method C) is presented
in this section. Method C uses the information about the tainted
flow to resolve the context where it appears within the URL, and
defines the corresponding range of characters to be replaced.
With method C, the tainted part (i.e., abcd) of URL 1 is interpreted
as a query parameter value, and will be completely replaced by the
exploit as follows:
Method C: http://example.com/1?payload=EXPLOIT&sp=x
In general the tainted characters may span multiple components
of the URL, so it is important to know where exactly the tainted
string is present in the source (i.e., the URL) and also in the sink.
To illustrate this, consider the following URL, where the source is
location.href and the tainted string is 1?payload=abcd:
http://example.com/1?payload=abcd&sp=x
(2)
Replacing the entire tainted string with the generated exploit will
cause the URL to redirect to a different location (which may no
longer contain the desired data-flow). To solve this, we define six
indices relevant for the exploit generation as follows:


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Talking About My Generation: Targeted DOM-based XSS Exploit Generation using Dynamic Data Flow Analysis
EuroSec’21, April 26, 2021, Online, United Kingdom
beginTaintURL
endTaintURL
replaceBeginURL
replaceEndURL
http://example.com/1?payload=abcd&sp=x
<script src='http://ads.example.com/getjs/1?payload=abcd'></script>
innerHTML (Sink):
replaceBeginParam
replaceEndParam
URL (Source):
query
path
host
scheme
Figure 2: An example of targeted exploit injection
• beginTaintURL and endTaintURL: Represent the start and
the end indices of the tainted string in the URL, and is computed
by searching for the tainted string within the URL.
• replaceBeginURL and replaceEndURL: Represent the start
and the end indices of the part of the tainted string that can
be replaced without causing the application to perform in an
unexpected way.
• replaceBeginParam and replaceEndParam: These are the
corresponding indices for replaceBeginURL and replaceEndURL
within the sink context and are used in the exploit generation
to analyze the preceding code and generate a correct breakOut
(Section 4).
In a final step, the generated exploit is inserted into the URL, replac-
ing the characters between the replaceBeginURL and replaceEn-
dURL indices.
An example for the URL in 2 is shown in Figure 2. The tainted
string 1?payload=abcd is first identified in the URL and the cor-
responding computed values for beginTaintURL and endTain-
tURL. The string abcd is identified as replaceable, with indices of
replaceBeginURL and replaceEndURL, which correspond to re-
placeBeginParam and replaceEndParam of the innerHTML sink
string. This position is used to generate a context-specific exploit,
replacing replaceBeginURL and replaceEndURL in the URL.
While the example shows replacement of a query parameter, it will
also successfully replace characters in other regions of the URL, for
example in the fragment.
The main advantage of method C is that the exploit is injected into
the URL in a position where it is most likely to result in successful
code execution. For example, consider the case where the URL
query contains a JSON encoded data structure. URLs generated by
methods A and B will simply replace the tainted portion of the
string, resulting in invalid JSON and causing the program to error
before the payload can reach the sink. In comparison, method C will
replace the appropriate parameter of the data structure, producing
valid JSON and successful code execution.
In addition to targeted URL insertion, method C offers several
advantages over existing A and B. For example, if the decodeURI
or decodeURIComponent is detected as part of the taint flow, the
corresponding encoded string will be searched for and replaced in
the exploit URL.
6
RESULTS AND DISCUSSION
Using the techniques described above, large-scale exploit detection,
generation and validation was performed on real-world websites,
with two goals: to re-measure the performance of existing exploit
Table 1: Crawling results comparison with previous studies
This work
DOMsday
25m Flows
C
B [9]
A [8]
Date
26/10/2020
01/08/2017
04/11/2013
Seed domains
100,000
10,000
5000
Sub-pages up to
10
5
all depth 1
Web pages
390,092
44,722
504,275
Pages/Domain
3.90
4.47
100.86
Frames
1,111,821
319,481
4,358,031
Taint Flows
20,912,107
4,140,873
24,474,873
Flows/Page
53.61
92.59
48.53
Flows/Frame
18.81
12.96
5.62
generation techniques on today’s websites, and to assess the ef-
fectiveness of the new method described in Section 5. The study
was carried out in two phases: first, a list of the 100,000 top ranked
domains by Tranco [12] was crawled1, and the corresponding taint
flows collected. The second phase consisted of selecting the rele-
vant flows from the first phase, generating the exploits using the
three different methodologies and validating these exploits.
Each top-level domain was crawled, and up to 10 links hosted on the
same domain as the starting page were selected for further visits.
A total of 390,092 pages were visited from 100,000 domains over 4
days in October 2020. Table 1 presents a comparison between the
crawling results done in this work versus the crawling results from
the studies of Lekies et al. [8] and Melicher et al. [9].
Following the methodology of [8, 9], we first filter the tainted
flows to select only those with URL-based sources and HTML
or JavaScript sinks (F1). Flows containing escape, encodeURI or
encodeURIComponent operations without a subsequent unescape,
decodeURI or decodeURIComponent are also excluded (F2), as these
functions encode characters which are necessary for exploit execu-
tion (e.g., < is encoded as %3C). Duplicate flows are removed as well
applying the prescription of [8] based on the domain, code location
and breakout sequence (F3). The number of flows after each filter
is as follows:
20,912,107
𝐹1
→338,906
𝐹2
→227,510
𝐹3
→15,710
Exploits were generated using each method for the remaining 15,710
flows and their effectiveness validated. An overview of exploit
validation results for each method is shown in Figure 3.
Overall, 7199 out of 15,710 (45.82%) of flows could be successfully
validated using method C, across 711 unique domains. This success
rate represents an improvement of 1.9 over method A (24.43%) and
1.8 over method B (25.72%). In addition, 846 (11.15%) of flows could
only be validated using method C, and were not successful with
either of the previous methods. As method C replaces characters
in the URL directly with an exploit, it is more likely to reach the
corresponding sink function without causing decoding or parsing
errors. Overall, 7588 (48.30%) unique flows out of 15,710 could be
successfully validated when combining all three methods.
1Available at: https://tranco-list.eu/list/K9ZW, list downloaded on 7th October 2020.


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EuroSec’21, April 26, 2021, Online, United Kingdom
Souphiane Bensalim, David Klein, Thomas Barber, and Martin Johns
Figure 3: Venn diagram of the validated flows using methods
A (25m flows), B (DOMsday) and C (this work)
A small fraction of flows could not be validated by method C, but
were successful with the other methods: 168 flows could only be
validated with method A (2.21% of all validated flows), and 221
flows could only be validated with method B (2.91% of all validated
flows). After manually analyzing these cases, we found that the URL
generated by method C in some situations breaks the application
logic, e.g., when a parameter value was used to redirect the visitor
to a specific page.
7
LIMITATIONS
A general issue when validating exploit URLs is the issue of concept
drift. When loading a dynamic website several times, its content
frequently changes between visits, e.g., due to different advertise-
ments being served. Some vulnerable flows are therefore invisible
on subsequent attempts to visit the website.
Modern Web browsers implement URL-Encoding as required by
RFC 1738 [3] to encode some reserved characters that might be
used in an unsafe way. While this mechanism allows parsing the
URL components correctly, it also defends against some XSS attacks
as the exploit injected into the URL is subsequently encoded. In this
work, we disabled the URL encoding mechanism firstly for compar-
ison reasons with the two previous works and secondly because
websites shouldn’t rely on an external security mechanism. Internet
Explorer for example doesn’t implement any URL-Encoding and at
the time of writing still holds a market share of 1,89% for desktop
browsers worldwide [14].
In addition, we consider only first level flows, that is flows that
directly go into a sink allowing script execution. Second level flows,
e.g., flows where a tainted value is stored in a cookie or local storage
and later inserted into a sink (potentially on a different part of the
website) are out of scope for this work. We refer to the work by
Steffens et al. [15] for an investigation of such second level flows.
One potential drawback of method C is that changes to the query
portion of the URL are sent to the server (changes to the fragment
are not), which could be detected and blocked by server-side de-
fense mechanisms, such as Web Application Firewalls (WAFs). The
fragment can also be transmitted to the server for methods A and
B, however, as it can be packaged into an XMLHttpRequest (XHR).
This is especially relevant for single-page applications, which make
extensive use of XHR.
8
RELATED WORK
DOM-based XSS vulnerabilities have been a topic of active research
over the last decade. While Amit Klein initially coined the term
in 2005 [7], dynamic taint tracking to detect DOM-based XSS vul-
nerabilities has been used since 2012 when Di Paola introduced
DOMinator [4], a Firefox Extension able to detect such vulnerabili-
ties. Lekies et al. [8] were the first to investigate the prevalence of
this vulnerability class on a large scale. Melicher et al. [9] did im-
prove on their methodology in 2018 and confirmed that DOM-based
XSS vulnerabilities remain a highly relevant issue.
DOM-based XSS studies have become increasingly elaborate over
time. Building on the work of Lekies et al. [8], Stock et al. [16]
studied their history as well as the code patterns resulting in vul-
nerabilities [18] while Steffens et al. [15] investigated persistent
DOM-based XSS vulnerabilities. To overcome the performance im-
pact of taint tracking, Melicher et al. [10] suggested a method where
tainting is only applied to websites suspected to contain vulnera-
bilities, based on a Machine Learning prefilter.
In addition to studying the prevalence of DOM-based XSS vul-
nerabilities, several attempts have been made to protect websites.
Chrome as well as Internet Explorer used to include XSS Filter
mechanisms, which inspected the data and blocked suspicious re-
quests. Due to dynamic content generation being the foundation
of the Web 2.0, the high number of false positives lead ultimately
to their removal [2, 17]. Attempts to automatically retrofit saniti-
zation functions before calling sinks, as attempted by Musch et al.
in ScriptProtect [11] breaks a significant portion of the websites in
question.
Browser vendors are currently working on two promising approaches
to counteract DOM-based XSS vulnerabilities. Trusted Types [5]
proposes integration of a policy mechanism into the type system. If
the developer wants to turn text into a DOM element, the text has
to conform to the given policy, e.g., not contain any markup. An-
other proposal is to include a sanitizer API directly in the browser.
This would allow developers to protect a Web application without
having to rely on the error prone process of writing their own san-
itization functionality or having to introduce additional external
libraries such as DOMPurify [6].
9
CONCLUSION
We investigated the top 100,000 websites and analyzed them for
DOM-based XSS vulnerabilities using the presented methodology
to generate and validate exploits. In total, 7199 exploits have been
successfully validated out of the 15,710 relevant for an exploit gen-
eration, representing a success rate of 45.82%. This represents an
improvement over the two previous exploit generation techniques
of [8, 9] by factors of 1.9 and 1.8 respectively. In addition, the new
technique presented here successfully generated exploits for 846
(11.15%) URLs, which were not successful with either of the previous
methods.
Despite rising of awareness about the dangers of DOM-based XSS
vulnerabilities over the last decade many of the top ranked websites
still suffer from such vulnerabilities.


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Talking About My Generation: Targeted DOM-based XSS Exploit Generation using Dynamic Data Flow Analysis
EuroSec’21, April 26, 2021, Online, United Kingdom
ACKNOWLEDGMENTS
We acknowledge funding by the Deutsche Forschungsgemeinschaft
(DFG, German Research Foundation) under Germany’s Excellence
Strategy - EXC 2092 CASA - 390781972 and from the state of Lower
Saxony under the project Mobilise.
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