Our (in)Secure Web: Understanding Update
Behavior of Websites and Its Impact on Security
Nurullah Demir1, Tobias Urban1, Kevin Wittek1,2, and Norbert Pohlmann1
1Institute for Internet Security—if(is)
Westphalian University of Applied Sciences Gelsenkirchen
2RWTH Aachen University
Software updates take an essential role in keeping IT envi-
ronments secure. If service providers delay or do not install updates, it
can cause unwanted security implications for their environments. This
paper conducts a large-scale measurement study of the update behavior of
websites and their utilized software stacks. Across 18 months, we analyze
over 5.6M websites and 246 distinct client- and server-side software dis-
tributions. We found that almost all analyzed sites use outdated software.
To understand the possible security implications of outdated software, we
analyze the potential vulnerabilities that aﬀect the utilized software. We
show that software components are getting older and more vulnerable
because they are not updated. We ﬁnd that 95 % of the analyzed websites
use at least one product for which a vulnerability existed.
Keywords: updates ·vulnerabilities ·security ·web measurement.
Nowadays, we use the Web for various tasks and services (e.g., talking to our
friends, sharing ideas, to be entertained, or to work). Naturally, these services
process a lot of personal and valuable data, which needs to be protected. There-
fore, web services need to be hardened against adversaries, for example, due to
imperfections of software. An essential role in every application’s security concept
is the updating process of the used components [
]. Not updating software might
have severe security implications. For example, the infamous Equifax data breach
that aﬀected 143 million people was possible because the company used software
with a known vulnerability that has already been ﬁxed in a newer version .
However, keeping software up to date is not always easy and, from the security
perspective, not always necessary (i.e., not every update ﬁxes a security issue).
Modern applications require a variety of diﬀerent technologies (e.g., libraries, web
servers, databases, etc.) to operate. Updating one of these technologies might
have unforeseeable eﬀects and, therefore, updates might create potentially high
overhead (e.g., if an update removes support of a used feature). More speciﬁcally,
service providers might object to install an update because they do not directly
2 Demir et al.
proﬁt from the new features (e.g., changes in an unused module). Hence, it is
reasonable not always to install every available update (e.g., to ensure stability).
In this work, we show that this challenge can have grave implications. To
understand how up to date the utilized software on the Web is and to understand
its possible security implications, we conduct a large-scale measurement. Previous
work also analyzed update behavior on the Web (e.g., [
]) but – to the best
of our knowledge – our measurement is more comprehensive than the previous
studies. While we analyze over 5.6M sites and nearly 250 software (SW) products,
other work in this ﬁeld often only analyzed one speciﬁc type of software or a
small subset. Therefore, our results are more generalizable and provide a better
overview of the scale of the problem.
To summarize, we make the following contributions:
We conduct a large-scale measurement that evaluates 246 software products
used on 5.6M websites over a period of 18 months, to determine update
behavior and security impact of not updating.
We show that 96 % of the analyzed websites run outdated software, which is
often more than four years old and is getting even older since no update is
We show that a vast majority of the analyzed websites (95 %) use software
for which vulnerabilities have been reported, and the number of vulnerable
websites is increasing over time.
In this section, we discuss the principles of how web applications work and how
known vulnerabilities are publicly managed, both necessary to appreciate our
We start by introducing key terminology. In this work, we use the term site (or
website) to describe a registerable domain, sometimes referred to as eTLD+1
(“extended Top Level Domain plus one”). Examples for sites are
. Each site may have several subdomains (e.g.,
). Following the deﬁnition of RFC 6454 [
], we call the tuple
of protocol (e.g., HTTPS), subdomain (or hostname), and port origin. This
distinction is important since the well-known security concept Same-Origin
Policy (SOP) guarantees that pages of diﬀerent origins cannot access each other.
We use the term page (or webpage) to describe a single HTML ﬁle (e.g., a webpage
hosted at a speciﬁc URL).
2.2 Web Technologies & Updating
To implement modern web applications, service providers rely on a diverse set
of server-side (e.g., PHP or MySQL) and client-side technologies (e.g., HTML
Understanding Update Behavior of Websites and Its Impact on Security 3
complex and dynamic architecture, not always under full control of the service
provider (e.g., usage of third parties [
]). Furthermore, the update frequency of
web technologies is higher compared to desktop software . Web applications
are commonly composed of diﬀerent modules that rely on each other to perform
a given task. Hence, one vulnerability in any of these modules might undermine
the security of the entire web app, depending on the severity of the vulnerability.
Once a vulnerability of an application is publicly known or privately reported to
the developers (see also Section 2.3), the provider of that application (hopefully)
provides an update to ﬁx it. Therefore, service providers need to check the
availability of updates of the used components and their dependencies and
transitive dependencies regularly. However, it should be noted that not all
updates ﬁx security issues, and, therefore, it is not necessary or desired (e.g., for
stability reasons) to install all updates right away.
2.3 Common Vulnerabilities and Exposures
Once vulnerabilities in software systems are discovered, reported to a vendor, or
shared with the internet community publicly, they are published in vulnerability
database platforms (e.g., in the National Vulnerability Database (NVD)). The
NVD utilizes the standardized Common Vulnerabilities and Exposures (CVE) data
format and enriches this data. Each CVE entry is provided in a machine-readable
format and contains details regarding the vulnerability (e.g., vulnerability type,
vulnerability severity, aﬀected software, and version(s)). The primary purpose of
each CVE entry is to determine which software is aﬀected by a vulnerability and
helps to estimate its consequences. Each entry in the NVD database is composed of
several data ﬁelds, of which we now describe the one most important for our work.
In the NVD database, the ﬁeld
of a CVE entry uniquely identiﬁes the entry
and also states the year when the vulnerability was made public, followed by a
sequence number (e.g.,
), the ﬁeld
CVE data timestamp
when the CVE entry was created. Furthermore, each CVE entry also includes a
list of known software conﬁgurations that are aﬀected by the vulnerability (ﬁeld
), formally known as Common Platform Enumeration (CPE).
CPE deﬁnes a naming scheme to identify products by combining, amongst other
values, the vendor, product name, and version. For example the CPE (in version
identiﬁes the product
provided by the vendor nodejs in version 4.0.0. Furthermore, the
ﬁeld lists all conditions under which the given vulnerability can be exploited
(e.g., combination of used products). Finally, the ﬁeld
practical implications of the vulnerability (e.g., a description of the attack vector)
and holds a score, the Common Vulnerability Scoring System (CVSS), ranging
from 0 to 10, which indicates the severity of the CVE (with ten being the most
severe). Again, it is worth noting that it not deﬁnite that if one uses a software
product – for which a vulnerability exists – that it is exploitable by an attacker.
For example, if an SQL-Injection is possible via the comment function of a blog,
4 Demir et al.
it can only be exploited if the comment function is enabled. Thus, our results
can be seen as an upper bound.
In this work, we want to assess the update behavior of web applications, measure
if they use outdated software, and test the security implications of using the
vulnerable software. To accomplish that, we collect the used modules (software
and version) of the websites present in the HTTPArchive [
] over a period of 18
month, extract known vulnerabilities from the National Vulnerability Database
database, and map them against the used software versions of the analyzed sites.
Identifying Used Software To assess the update behavior of websites, we need to
identify the software versions of the software in use. To do so, we utilized data
provided by HTTPArchive [
], which includes all identiﬁed technologies used by
a website. HTTPArchive crawls the landing page of millions of popular origins
(mobile and desktop) based on the Chrome User Experience Report (CrUX) [
every month, since January 2019. In CrUX, Google provides publicly metrics like
load, interaction, layout stability of the websites that are visited by the Chrome
web browser users on a monthly basis. This real-world dataset includes popular
and unpopular websites [
]. In our study, we analyze all websites provided in
HTTPArchive. Hence, we can use 18 data points in our measurement (M#1 –
M#18). The data provided by HTTPArchive includes, among other data: (1)
the date of the crawl, (2) the visited origins, and (3) identiﬁed technologies
(software including its version). HTTPArchive uses Wappalyzer [
] to identify
the used software, which uses diﬀerent information provided by a site to infer the
user version and technology stack. In order to make version changes comparable,
we converted the provided data to the semantic versioning (SemVer) standard
] and validate also the version information from
HTTPArchive as well as from NVD and check if provided versions are in a valid
SemVer format. This uniﬁcation allows us to map the observed versions of the
known vulnerabilities. If we ﬁnd an incomplete SemVer string, we extended it
with “.0” until it ﬁts the format.
Identifying Vulnerable Software To better understand the security impact of
updates, we map the software used by an origin to publicly known vulnerabilities.
We collect the vulnerabilities from the National Vulnerability Database (NVD)
Each entry in the NVD holds various information, but only three are essential
to our study: (1) the date on which it was published, (2) a list of systems that
are aﬀected by it, and (3) the impact metrics how it can be exploited and its
severity. In this work, we only focus on vectors that can be exploited by a remote
3We used the database published on 04/07/20.
Understanding Update Behavior of Websites and Its Impact on Security 5
3.1 Dataset Preparation and Enrichment
Here, we describe the steps taken to enrich our dataset to make it more reliable.
Release History To get a ﬁrm understanding of the update behaviour of websites,
it is inevitable to know the dates on which diﬀerent versions of a software were
released (“release history”). To construct the list of release dates of each software
product, we used GitHub-API for the oﬃcial repositories, on GitHub, of the
products and extracted the date on which a new version was pushed to it and
store the corresponding SemVer. If a product did not provide an open repository
on GitHub, we manually collected the oﬃcial release dates from the product’s
oﬃcial project webpages, if it’s published.
Dataset Preparation Since the Web is constantly evolving and Web measurements
tend to be (strongly) impacted by noise, we only analyzed software products on a
site for which we found version numbers in at least four consecutive measurements.
Furthermore, we dropped all records with polluted data (e.g., blank, invalid
versions, duplicates, dummy data) from our dataset. Finally, in order to make
a valid match between CVE entries and software in our dataset, we manually
assigned each software in our dataset their CPE (naming scheme) using the CPE
Dictionary  provided by NVD.
3.2 Analyzing Updating Behavior & Security Implications
In this section, we describe how we measure update behavior and identify vulner-
Updating Behavior To understand update behavior in our dataset, one needs to
measure the deployed software’s version changes over time. Utilizing the release
dates of each software product, we know, at each measurement point in our
dataset, whether a site/origin deploys the latest software version of a product or
if it should be updated. If we found that an outdated product is used, we check
if it was updated in the subsequent measurements (i.e., if the SemVer increases).
This approach allows us to test if a product is updated after all and to check
how long this process took. In our analysis, we call an increasing SemVer an
update and decreasing version number a downgrade. In this analysis, we compare
part of a product’s SemVer, utilizing the release dates of
each version, and not the
section because service providers might not use
the latest major release due to signiﬁcant migration overhead. For example, we
would consider that an origin is “up to date” if it runs version 1.1.0 of a product
even if version 2.1.0 (major release change) is available. However, if version 1.1.1
would be available, we consider it “out of date”.
Identifying Vulnerable Websites One way to measure the impact of an update
on the security of a site is to test if more or less vulnerabilities exists for the
new version, in contrast to the old version. To identify vulnerable software on
6 Demir et al.
a website, we retrieve the relevant CVEs for the identiﬁed software and then
check if it is deﬁned in these CVE entries – with consideration of version-
Start[Excluding/Including] and versionEnd[Excluding/Including] settings. We
map a vulnerability to a crawled origin if and only if (1) it uses a software for
which a vulnerability exists and (2) if it was published before the crawl was
conducted. Utilizing the Common Vulnerability Scoring System (CVSS) of each
vulnerability, we can also assess the theoretical gain in security.
After describing our approach to analyze the update behavior of websites and
its possible security impact on websites, this section introduces the large-scale
measurement results. Overall, we observed 8.315.260 origins on 5.655.939 distinct
domains using 342 distinct software products. After ﬁltering, we were left with
8.205.923 origins (99 %) on 5.604.657 domains (99 %) using 246 (72 %) software
products. We collected 31.909 releases for 246 software products. Furthermore,
we collected 147.312 vulnerabilities of which 2.793 (2%) match to at least one
identiﬁed product. Overall, we found an exploitable vulnerability for 148 (60 %) of
the analyzed software products. Note that products with no public release history
are excluded from analyzing update behavior and security analysis if they don’t
have a known vulnerability. Note also we have full access to all the segments of
for 98.5% of our data. In total, we identiﬁed 12.062.618
software updates across all measurements. Table 1 provides an overview of all
evaluated records of each measurement run.
4.1 Update Behavior on the Web
In the following, we analyze the impact of adoption of releases on the Web on
website level and from software perspective.
Update Behavior of Websites
The ﬁrst step to understand the update
behavior of websites is to analyze the fraction of used software products that
are fully patched, according to our deﬁnitions. Remember that we assume that
a software product should be updated if a newer minor version or patch is
available (i.e., we exclude the major version (see Section 3.2)). In our dataset, we
identiﬁed a median of 3 (min: 1, max: 17, avg: 3.37) evaluable software products
for each website. Overall, we identiﬁed that across all measurement points, on
average, 94% of all observed websites were not fully updated (i.e., at least for
one software product exists a newer version). Only 6 % of the observed sites used
only up to date software while 47% entirely relied on outdated software types.
The mean fraction of out of date software products is 74 % for each observed
website across our measurement points. These numbers show that websites often
utilize outdated software. While at domain granularity, almost all analyzed sites
use outdated software, it is interesting to analyze if subdomains show diﬀerent
update behavior. Figure 1 compares the fraction of up to date software utilized
Understanding Update Behavior of Websites and Its Impact on Security 7
M. Date #Sites #Origins #Products #dist. Ver. #Updates #Vuln.
M#1 01/19 2.5M 3.4M 208 15,436 — 2,201
M#2 02/19 2.3M 3.1M 204 15,178 0.4M 2,224
M#3 03/19 2.3M 3.1M 205 15,390 0.5M 2,235
M#4 04/19 2.7M 3.5M 205 16,145 0.6M 2,291
M#5 05/19 2.8M 3.6M 216 16,741 0.4M 2,298
M#6 06/19 2.8M 3.6M 217 17,013 0.7M 2,310
M#7 07/19 3.0M 3.9M 215 17,438 0.6M 2,286
M#8 08/19 3.0M 3.9M 215 17,474 0.5M 2,316
M#9 09/19 3.0M 3.9M 215 17,682 1.0M 2,390
M#10 10/19 3.0M 3.8M 217 17,873 0.8M 2,424
M#11 11/19 3.0M 3.8M 217 17,958 1.0M 2,468
M#12 12/19 3.0M 3.8M 216 18,122 1.0M 2,478
M#13 01/20 2.9M 3.8M 217 18,173 0.8M 2,502
M#14 02/20 2.7M 3.4M 211 17,558 0.4M 2,526
M#15 03/20 3.1M 3.9M 217 18,558 0.4M 2,412
M#16 04/20 3.3M 4.2M 217 19,321 0.6M 2,467
M#17 05/20 3.1M 4.0M 220 19,353 0.8M 2,460
M#18 06/20 3.4M 4.4M 218 20,118 0.6M 2,475
Table 1. Overview of all measurement points.
on subdomains (e.g., bar.foo.com) against the root domains (e.g., foo.com), along
our measurement points. In the ﬁgure, zero means that all software is up to date
and one means that all software is outdated. Our data shows that most software
products are not updated to the newest release, but it is still interesting to
analyze the update cycles websites use in the ﬁeld. On average, we observed 0.7M
version changes between two measurement runs. 97 % of them were upgrades
(i.e., the SemVer increased) and consequently 3 % were downgrades.
Update Behavior from a Software Perspective
Previously, we have shown
that websites tend to use outdated software. In the following, we take a closer
look at the used software to get a better understanding if the type of used
software has an impact on its update frequency. Across all measurements, the
software used on the live systems is 44 months old (M#1: 40, M#18: 48), and
the trend during the measurement is that it gets even older (18 days each month
on average). To determine how the average age changes by software types, we
measured the average age of the top ten used software types for all measurement
points. These top ten account for 65 % of all analyzed software types. In Figure 2
we show the corresponding results. Our ﬁnding clariﬁes that client-side software
A closer observation of the releases SW shows that the server-side software has
shorter release cycles than client-side software in the measured period (e.g., nginx
has 18 and jQuery only has 6 releases). While the age of the software itself is not
necessarily a problem per se, it is notable that the average number of months a
utilized software is behind the latest patch is 48. The ANOVA test (
8 Demir et al.
Fractions of utilized outdated software products on the analyzed domains in
comparison to their subdomains (1 = no product is up-to-date).
showed no statistical evidence that the popularity of a website, according to
the Tranco list [
], has an impact on the age of the used software (i.e., popular
and less popular websites use outdated software alike). Using software that is
four years old might be troubling, given that on average 41 newer version exists,
because the software might have severe security issues. We have shown that
overall mostly outdated software is used. However, it is interesting to understand
if this applies to all types of software alike or if speciﬁc products are updated
Adoption of Software Releases
To get a better understanding of the update
behavior of websites, we observe the adoption of releases. We ﬁnd that every
month, on average, 67 % of the software used has a new release. However, our
observations show that only a few service providers install the release promptly.
We record that on average, only 7 % of available updates are processed (min: 4 %,
max: 11 %). The mean time between two updates for any of the used software
on one website is 3.5 months (SD: 5.4). To get a more in-depth understanding
of the adoption of software releases, we measure it in a time span of 30 days
after the release. Figure 3 shows the fraction of processed updates by websites
in that time span for the top eight software types. The top eight types account
for 60 % of all used software. In general, we see that PATCH level releases are
processed most frequently. Furthermore, we observe that the adoption of release
types diﬀer based on the software types. E-Commerce software process PATCH
Understanding Update Behavior of Websites and Its Impact on Security 9
Average age (in month) of top utilized 10 software types for all measurement
releases most frequently and search engine optimization software (SEO) MAJOR
releases respectively. We assume that integrated automatic background updates
play an important role why speciﬁc software types are updated. For example
WordPress and Shopware, two popular content management systems, provide an
auto update functionality [25,17].
Summary Based on our dataset, we have shown that the used software on the
Web is often very old and not updated frequently. While diﬀerences in the update
behavior between diﬀerent types of software exist, the majority of all times is still
not updated. However, the impact of this not-updating is not clear and needs
4.2 Security Impact of Not Updating
Experts agree that updating is one of the most critical tasks one should do to
harden a system or to avoid data leaks [
]. Therefore, we are interested in the
security impact of the identiﬁed tend to use outdated software.
Vulnerability of Websites
Towards understanding the threats that result from
the usage of outdated software, we ﬁrst analyze the scope of aﬀected websites. On
average, 94% of the analyzed websites contain at least one potential vulnerable
software, which was slightly increasing over the course of our measurements
10 Demir et al.
Fig. 3. Fraction of processing a new release for top used eight software types.
(M#1: 92 % to M#18: 95 %). We also record that each analyzed software has on
average 8 vulnerabilities and that websites are aﬀected, on average, by 29 (min:
0, max: 963). Our data shows that the number of exploitable vulnerabilities is
decreasing over time for both per software (0.4 per month) and per websites
(0.14 per month). Hence, overall the number of websites that have at least one
vulnerability increases but the amount of vulnerabilities per site decreases.
Each vulnerability has a diﬀerent security impact on a website, and, therefore,
the number of identiﬁed vulnerabilities does not directly imply the severeness of
them. The NVD assigns a score to each vulnerability to highlight its severeness
(i.e., the CVSS score). Figure 4 shows the mean CVSS scores for the analyzes
websites their rank. By inspecting the ﬁgure, one can see that less popular sites
(the rank is higher) are aﬀected by more severe vulnerabilities. The Spearman
05) showed a statistical signiﬁcant correlation between the rank and
the mean CVSS score of the identiﬁed vulnerabilities (
007). Table 2,
in Appendix A lists the most common vulnerabilities in our last measurement
point (M#18). A stunning majority of websites (92 %) is theoretically vulnerable
to Cross-site Scripting (XSS) attacks. In our dataset, jQuery is the software that
is most often aﬀected by a CVE (92 %). A list of the most prominent CVEs is
given in Appendix C. Given the wide occurrence of vulnerabilities in our dataset,
the question arises which threats websites and users actually face.
Understanding Update Behavior of Websites and Its Impact on Security 11
AVG CVSS by popularity of websites. Vulnerability severity is signiﬁcantly
lower for high-ranked websites.
Analysis of Available Vulnerabilities
Figure 5, shows the distribution of
severity of identiﬁed vulnerabilities on websites based on the Common Vulnera-
bility Scoring System (CVSS). Our results show that the number of websites with
the most severe vulnerability (CVSS: 10) steadily decreases. The average number
of vulnerable websites with a severity “HIGH” (CVSS: 7–10) is decreasing (M#1:
43 %, M#18: 39 %), while the number of vulnerable websites with “MEDIUM”
(CVSS: 4–7) remains almost constant (M#1: 47 %, M#18: 49 %). For this analysis,
we only used the most severe vulnerability for each website.
Given the result that the average age of used software depends on its type
(see 2), we ﬁnd that older software has more dangerous vulnerabilities. For
50 in M#1 and
62 in M#18, while the score and age for programming languages go from 9
33. This conﬁrms that older software does have more vulnerabilities and
highlights the need for better update processes of websites. Furthermore, our
analysis shows that performing updates has a signiﬁcant impact on the security of
software. The average value of CVSS for software for which an update is available
is 6.4 (“MEDIUM”). However, after applying the update(s), the CVSS is lowered
to 2.4 (“LOW”).
12 Demir et al.
Fraction of CVSS score distribution on websites for all measurement points (10
= Critical, 0=No Vulnerability).
Although we have put a great eﬀort while preparing our dataset, our study is
impacted by certain limitations. Our approach comes with the limitation that,
on the one hand, HTTPArchive only crawls landing pages and does not interact
with the website, which might hide the complexity of an origin [
], and, on
the other hand, Wappalyzer might not detect all used software for the website.
Although NVD is one of the most popular vulnerability databases, there are
some discussions around the accuracy of the data provided by NVD e.g., [
In our study, we assume that software utilized by a website is vulnerable if the
NVD provides a CVE entry for it. For ethical reasons, we did not validate if
successful exploitation of the CVE requires any interaction or enabled functions.
We also don’t examine any mechanism for the validity of CVE entries.
Understanding Update Behavior of Websites and Its Impact on Security 13
6 Related Work
To the best of our knowledge, our study is the ﬁrst one that measures update
behavior and security implications by evaluating all utilized server and client-side
software on a website and by conducting multiple measurements. In the following,
we discuss studies related to our research.
Update Behavior Update behavior of software has been previously studied.
Tajalizadehkhoob et al. [
] measure the security state of software provided by
hosting providers to understand the role of hosting providers for securing websites.
Vaniea et al. [
] conduct a survey to understand the update behaviour of software.
They ask 307 survey respondents to provide software update stories and analyze
these stories to determine the possible motivations for software updates. Stock
et al. [
] examine the top 500 websites per year between 1997 and 2016 utilizing
archive.org dataset. In their measurement, they mainly evaluate security headers
and analyse usage of outdated jQuery libraries.
Security Implications Prior literature has proposed various techniques to measure
websites’ security in terms of diﬀerent metrics. Lauinger et al.  study widely
Top 75k. Van Goethem et al. [
] report the state of security for 22,000 websites
that originate in 28 European countries. Their analysis is based on diﬀerent
metrics (e.g., security headers, information leakage, outdated software). However,
they use only a few popular software products for their measurement. Huang
et al. [
] measure the security mechanisms of 57,112 chinese websites based on
vulnerabilities published on Chinese bug bounty platforms between 2012 and
2015. Van Acker et al. [
] scrutinize the security state of login webpages by
attacking login pages of websites in the Alexa top 100k.
7 Discussion and Conclusion
In this work, we measured the update behavior and possible security implications
of software products utilized on more than 5.6M websites. Our measurement
highlights the current state of the Web and shows the update behavior of websites
over the course of 18 month. We show that most of the Web’s utilized software
is outdated, often by more than four years. Running outdated software is not a
security problem per se because the old software might not be vulnerable. However,
we found several sites that use software products for which vulnerabilities have
been reported. Furthermore, we show that the number of vulnerable websites
increases over time while the average severity of identiﬁed vulnerabilities decreases.
For instance, we record that 95 % of websites potentially contain at least one
vulnerable software. It has to be noted that the identiﬁed vulnerabilities in our
work must be seen as an upper bound because utilizing a product for which
vulnerabilities exist does not automatically mean that it can be exploited (e.g., the
vulnerable module of the product is deactivated or not used). Our results still
14 Demir et al.
highlight that website providers need to take more care about their update
processes, even if this comes with a potential overhead, to protect their users and
This work was partially supported by the Ministry of Culture and Science
of North Rhine-Westphalia (MKW grant 005-1703-0021 “MEwM” and “con-
nect.emscherlippe”) and by the Federal Ministry for Economic Aﬀairs and Energy
(grant 01MK20008E “Service-Meister”).
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A Overview of the Top Identiﬁed CWEs
In this appendix, we show our ﬁndings related to identiﬁed CWEs. Table 2 lists
the most common CWEs on websites that we identiﬁed in the last measurement
16 Demir et al.
Vulnerability Type (CWE) Relative Frequency
CWE-79 Improper Neutralization of Input During Web Page Generation (‘Cross-site Scripting’) 0.92
CWE-20 Improper Input Validation 0.32
CWE-400 Uncontrolled Resource Consumption 0.27
CWE-200 Exposure of Sensitive Information to an Unauthorized Actor 0.24
CWE-476 NULL Pointer Dereference 0.24
CWE-601 URL Redirection to Untrusted Site (‘Open Redirect’) 0.22
CWE-125 Out-of-bounds Read 0.22
CWE-119 Improper Restriction of Operations within the Bounds of a Memory Buﬀer 0.20
CWE-787 Out-of-bounds Write 0.19
CWE-190 Integer Overﬂow or Wraparound 0.17
CWE-284 Improper Access Control 0.17
Top 10 vulnerabilities in our last measurement point (M#18) by relative
frequency on websites.
Software CVE CVE Publication CWE CVSS Public exploit Vuln. Websites Total usage
jQuery CVE-2020-11023 04.2020 XSS 4.3 73.98M 4M
Apache CVE-2017-7679 06.2017 Buﬀer Over-read 7.5 30.26M 0.46M
PHP CVE-2015-8880 05.2016 Double free 10 30.45M 0.46M
PHP CVE-2016-2554 03.2016 Buﬀer Over-read 10 30.23M 0.46M
WordPress CVE-2018-20148 12.2018 Deserialization of Untrusted Data 7.3 30.18M 0.46M
WordPress CVE-2019-20041 12.2019 Improper Input Validation 7.3 70.31M 0.46M
Some examples of vulnerabilities identiﬁed on analyzed websites that run
run (June 2020). While the vulnerability Cross-site Scripting (XSS) occurs in
almost all websites, a closer analysis of the same measurement point (M#18)
shows that only 28% of software is vulnerable to this vulnerability.
B Average age of the 20 used software by website-ranking
Figure 6 shows the most popular software types, their average age (in month),
and the rank of the websites on which they are used. We record that most of the
widely used software on the web is often very old. We also found that the average
age of utilized software on a website is unrelated to its popularity, according to
the Tranco list .
C Case Studies
Table 3 illustrates the most common CVE entries identiﬁed in our study. CVE-
2020-11023 is the most common vulnerability with the severity “MEDIUM”
– based on our last measurement. Some of the vulnerabilities require certain
functions or enabled functions (e.g., CVE-2017-7679 for Apache requires mod -
mime and CVE-2016-2554 for PHP requires ﬁle uploading functionality) In
some cases, the running software requires interaction between more than one
component to abuse an exploit. The listed vulnerabilities for WordPress and
vulnerability CVE-2015-8880 for PHP do not require any interaction or enabled
features and can be exploited directly.
Understanding Update Behavior of Websites and Its Impact on Security 17
Average age (in month) of the top 20 used software by website ranking. The
share of software in our dataset is shown in brackets – Blank cells: no website identiﬁed
in the corresponding ranking.