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title: "Tales of Favicons and Caches: Persistent Tracking in Modern Browsers"
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    title: "Tales of Favicons and Caches: Persistent Tracking in Modern Browsers"
    author: Konstantinos Solomos, John Kristoff, Chris Kanich, Jason Polakis
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  - John Kristoff
  - Chris Kanich
  - Jason Polakis
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# Tales of Favicons and Caches: Persistent Tracking in Modern Browsers

**Tales of Favicons and Caches: Persistent Tracking in Modern Browsers** - Konstantinos Solomos, John Kristoff, Chris Kanich, Jason Polakis, NDSS Symposium.

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# Tales of Favicons and Caches: Persistent Tracking in Modern Browsers

--- page 1 ---

Statement from the NDSS 2021 Program Committee:
NDSS is devoted to ethical principles and encourages the research community to
ensure its work protects the privacy, security, and safety of users and others involved. While the NDSS 2021 PC appreciated the technical
contributions of this paper, it was the subject of a debate in our community regarding the responsible disclosure of vulnerabilities for the
Firefox web browser. The PC examined and discussed the ethics concerns raised and the authors' response. Although no harm came to users,
the authors' oversight could have made a non-vulnerable browser vulnerable to the attack proposed in the paper. The PC does not believe the
authors acted in bad faith. Nevertheless, we decided to add this note as well as the authors' response (in an Appendix) to the paper because
the NDSS PC takes both the ethics of responsible disclosure and fairness towards the authors seriously. It is the PC's view that researchers
must not engage in disclosure practices that subject users to an appreciable risk of substantial harm. NDSS will work with other conferences
to further improve the transparency of vulnerability disclosure to reduce such errors in the future.Tales of
F
A
V
I
C
O
N
S
and Caches:
Persistent Tracking in Modern Browsers
Konstantinos Solomos, John Kristoff, Chris Kanich, Jason Polakis
University of Illinois at Chicago
f
ksolom6, jkrist3, ckanich, polakis
g
@uic.edu
Abstract
—The privacy threats of online tracking have gar-
nered considerable attention in recent years from researchers and
practitioners. This has resulted in users becoming more privacy-
cautious and browsers gradually adopting countermeasures to
mitigate certain forms of cookie-based and cookie-less tracking.
Nonetheless, the complexity and feature-rich nature of modern
browsers often lead to the deployment of seemingly innocuous
functionality that can be readily abused by adversaries. In this
paper we introduce a novel tracking mechanism that misuses a
simple yet ubiquitous browser feature:
favicons
. In more detail,
a website can track users across browsing sessions by storing a
tracking identier as a set of entries in the browser's dedicated
favicon cache, where each entry corresponds to a specic sub-
domain. In subsequent user visits the website can reconstruct
the identier by observing which favicons are requested by the
browser while the user is automatically and rapidly redirected
through a series of subdomains. More importantly, the caching of
favicons in modern browsers exhibits several unique characteris-
tics that render this tracking vector particularly powerful, as it is
persistent (
not
affected by users clearing their browser data), non-
destructive (reconstructing the identier in subsequent visits does
not alter the existing combination of cached entries), and even
crosses the isolation of the
incognito
mode. We experimentally
evaluate several aspects of our attack, and present a series of
optimization techniques that render our attack practical. We
nd that combining our favicon-based tracking technique with
immutable browser-ngerprinting attributes that do not change
over time allows a website to reconstruct a 32-bit tracking
identier in 2 seconds. Furthermore, our attack works in all
major browsers that use a favicon cache, including Chrome and
Safari. Due to the severity of our attack we propose changes to
browsers' favicon caching behavior that can prevent this form
of tracking, and have disclosed our ndings to browser vendors
who are currently exploring appropriate mitigation strategies.
I. I
NTRODUCTION
Browsers lie at the heart of the web ecosystem, as they
mediate and facilitate users' access to the Internet. As the
Web continues to expand and evolve, online services strive to
offer a richer and smoother user experience; this necessitates
appropriate support from web browsers, which continuously
adopt and deploy new standards, APIs and features [76]. These
mechanisms may allow web sites to access a plethora of device
and system information [55], [21] that can enable privacy-
invasive practices, e.g., trackers leveraging browser features to
exltrate users' Personally Identiable Information (PII) [24].
Naturally, the increasing complexity and expanding set of
features supported by browsers introduce new avenues for
privacy-invasive or privacy-violating behavior, thus, exposing
users to signicant risks [53].
In more detail, while cookie-based tracking (e.g., through
third-party cookies [57]) remains a major issue [29], [9],
[69], tracking techniques that do not rely on HTTP cookies
are on the rise [63], [16] and have attracted considerable
attention from the research community (e.g., novel techniques
for device and browser ngerprinting [25], [18], [82], [23],
[50]). Researchers have even demonstrated how new browser
security mechanisms can be misused for tracking [78], and the
rise of online tracking [52] has prompted user guidelines and
recommendations from the FTC [20].
However, cookie-less tracking capabilities do not neces-
sarily stem from modern or complex browser mechanisms
(e.g., service workers [43]), but may be enabled by simple or
overlooked browser functionality. In this paper we present a
novel tracking mechanism that exemplies this, as we demon-
strate how websites can leverage
favicons
to create persistent
tracking identiers. While favicons have been a part of the
web for more than two decades and are a fairly simple website
resource, modern browsers exhibit interesting and sometimes
fairly idiosyncratic behavior when caching them. In fact, the
favicon cache (i) is a dedicated cache that is not part of the
Network and Distributed Systems Security (NDSS) Symposium 2021
21-25 February 2021, Virtual
ISBN 1-891562-66-5
https://dx.doi.org/10.14722/ndss.2021.24202
www.ndss-symposium.org

--- page 2 ---

browser's HTTP cache, (ii) is not affected when users clear
the browser's cache/history/data, (iii) is not properly isolated
from private browsing modes (i.e.,
incognito
mode), and (iv)
can keep favicons cached for an entire year [26].
By leveraging all these properties, we demonstrate a novel
persistent tracking mechanism that allows websites to re-
identify users across visits even if they are in incognito mode
or have cleared client-side browser data. Specically, websites
can create and store a unique browser identier through a
unique combination of entries in the favicon cache. To be more
precise, this tracking can be easily performed by any website
by redirecting the user accordingly through a series of subdo-
mains. These subdomains serve different favicons and, thus,
create their own entries in the Favicon-Cache. Accordingly, a
set of N-subdomains can be used to create an N-bit identier,
that is unique for each browser. Since the attacker controls
the website, they can force the browser to visit subdomains
without any user interaction. In essence, the presence of the
favicon for subdomain
i
in the cache corresponds to a value of
1
for the
i
-th bit of the identier, while the absence denotes a
value of
0
.
We nd that our attack works against all major browsers
that use a favicon cache, including Chrome, Safari, and the
more privacy-oriented Brave. We experimentally evaluate our
attack methodology using common hosting services and devel-
opment frameworks, and measure the impact and performance
of several attack characteristics. First, we experiment with the
size of the browser identier across different types of devices
(desktop/mobile) and network connections (high-end/cellular
network). While performance depends on the network condi-
tions and the server's computational power, for a basic server
deployed on Amazon AWS, we nd that redirections between
subdomains can be done within 110-180 ms. As such, for the
vanilla
version of our attack, storing and reading a full 32-bit
identier requires about 2.5 and 5 seconds respectively.
Subsequently, we explore techniques to reduce the overall
duration of the attack, as well as selectively assign optimal
identiers (i.e., with fewer redirections) to weaker devices.
Our most important optimization stems from the following
observation: while robust and immutable browser ngerprint-
ing attributes are not sufcient for uniquely identifying ma-
chines at an
Internet-scale
, they are ideal for augmenting low-
throughput tracking vectors like the one we demonstrate. The
discriminating power of these attributes can be transformed
into bits that constitute a portion of the tracking identier,
thus optimizing the attack by reducing the required redirections
(i.e., favicon-based bits in the identier) for generating a
sufciently long identier. We conduct an in-depth analysis
using a real-world dataset of over 270K browser ngerprints
and demonstrate that websites can signicantly optimize the
attack by recreating part of the unique identier from nger-
printing attributes that do not typically change over time [82]
(e.g., Platform, WebGL vendor). We nd that websites can
reconstruct a 32-bit tracking identier (allowing to differentiate
almost 4.3 Billion browsers) in

2 seconds.
Overall, while favicons have long been considered a simple
decorative resource supported by browsers to facilitate web-
sites' branding, our research demonstrates that they introduce a
powerful tracking vector that poses a signicant privacy threat
to users. The attack workow can be easily implemented by
any website, without the need for user interaction or consent,
and works even when popular anti-tracking extensions are
deployed. To make matters worse, the idiosyncratic caching
behavior of modern browsers, lends a particularly egregious
property to our attack as resources in the favicon cache are
used even when browsing in incognito mode due to im-
proper isolation practices in all major browsers. Furthermore,
our ngerprint-based optimization technique demonstrates the
threat and practicality of combinatorial approaches that use dif-
ferent techniques to complement each other, and highlights the
need for more holistic explorations of anti-tracking defenses.
Guided by the severity of our ndings we have disclosed our
ndings to all affected browsers who are currently working on
remediation efforts, while we also propose various defenses
including a simple-yet-effective countermeasure that can mit-
igate our attack.
In summary, our research contributions are:

We introduce a novel tracking mechanism that allows
websites to persistently identify users across browsing
sessions, even in incognito mode. Subsequently, we
demonstrate how immutable browser ngerprints intro-
duce a powerful optimization mechanism that can be used
to augment other tracking vectors.

We conduct an extensive experimental evaluation of our
proposed attack and optimization techniques under var-
ious scenarios and demonstrate the practicality of our
attack. We also explore the effect of popular privacy-
enhancing browser extensions and nd that while they
can impact performance they do not prevent our attack.

Due to the severity of our attack, we have disclosed our
ndings to major browsers, setting in motion remediation
efforts to better protect users' privacy, and also propose
caching strategies that mitigate this threat.
II. B
ACKGROUND
& T
HREAT
M
ODEL
Modern browsers offer a wide range of functionalities and
APIs specically designed to improve the user's experience.
One such example are favicons, which were rst introduced
to help users quickly differentiate between different websites
in their list of bookmarks [37]. When browsers load a website
they automatically issue a request in order to look up a
specic image le, typically referred to as the favicon. This
is then displayed in various places within the browser, such
as the address bar, the bookmarks bar, the tabs, and the most
visited and top choices on the home page. All modern web
browsers across major operating systems and devices support
the fetching, rendering and usage of favicons. When originally
introduced, the icon les had a specic naming scheme and
format (
favicon.ico
), and were located in the root directory of
a website [8]. To support the evolution and complex structure
of modern webpages, various formats (e.g.,
png, svg
) and sizes
are supported, as well as methods for dynamically changing
the favicon (e.g., to indicate a notication), thus providing
additional exibility to web developers.
To serve a favicon on their website, a developer has to in-
clude an
<link rel>
attribute in the webpage's header [84].
In general, the
rel
tag is used to dene a relationship between
an HTML document and an external resource like an image,
animation, or JavaScript. When dened in the header of the
2

--- page 3 ---

TABLE I: Example of Favicon Cache content and layout. Entry ID Page URL Favicon ID TTL Dimensions Size1
foo.com
favicon.ico
50000
16 X 16
120
2
xyz.foo.com fav_v2.ico 10000 32 X 32 240
3
foo.com/path
favicon.ico
25500
16 X 16
120 HTML page, it species the le name and location of the icon
le inside the web server's directory [59], [83]. For instance,
the code in Listing 1 instructs the browser to request the
page's favicon from the “resources” directory. If this tag does
not exist, the browser requests the icon from the predened
webpage's root directory. Finally, a link between the page and
the favicon is created only when the provided URL is valid
and responsive and it contains an icon le that can be properly
rendered. In any other case, a blank favicon is displayed.
Listing 1: Fetching the favicon from a custom location.<link rel="icon" href="/resources/favicon.ico"type="image/x-icon">As any other resource needed for the functionality and
performance of a website (e.g., images, JavaScript), favicons
also need to be easily accessed. In modern web browsers (both
desktop and mobile) these icons are independently stored and
cached in a separate local database, called the Favicon Cache
(
F-Cache
) which includes various primary and secondary
metadata, as shown in Table I. The primary data entries include
the Visited URL, the favicon ID and the Time to Live (TTL).
The Visited URL stores the explicitly visited URL of the active
browser tab, such as a subdomain or an inner path under the
same base domain (i.e., eTLD+1). These will have their own
cache entries whenever a different icon is provided. While this
allows web developers to enhance the browsing experience by
customizing the favicons for different parts of their website,
it also introduces a tracking vector as we outline in §III.
Moreover, as with other resources typically cached by
browsers, the favicon TTL is mainly dened by the
Cache-
Control, Expires
HTTP headers. The value of each header eld
controls the time for which the favicon is considered “fresh”.
The browser can also be instructed to not cache the icon
(e.g.,
Cache-Control: no-cache/no-store
). When
none of these headers exists, a short-term expiration date is
assigned (e.g., 6 hours in Chrome [5]). The maximum time
for which a favicon can be cached is one year. Finally, since
favicons are also handled by different browser components,
including the Image Renderer for displaying them, the F-Cache
stores other metadata including the dimensions and size of each
icon, and a timestamp for the last request and update.
Caching Policies.
Once a resource is stored in a cache, it
could theoretically be served by the cache forever. However,
caches have nite storage so items are periodically removed
from storage or may change on the server so the cache should
be updated. Similar to other browser caches, F-Cache works
under the HTTP client-server protocol and has to communicate
with the server to add, update or modify a favicon resource.
More specically, there is a set of Cache Policies that dene
the usage of the F-Cache in each browser. The basic rules are:
Create Entry
. Whenever a browser loads a website, it rst
reads the icon attribute from the page header and searches
Fig. 1: Expiration of favicon entries in the top 10K sites.
the F-Cache for an entry for the current page URL being
visited. If no such entry exists, it generates a request to fetch
the resource from the previously read attribute. When the
fetched resource is successfully rendered, the link between the
page and the favicon is created and the entry is committed
to the database along with the necessary icon information.
According to Chrome's specication [5] the browser commits
all new entries and modications of every linked database (e.g.,
favicon, cookies, browsing history) every 10 seconds.
Conditional Storage
. Before adding a resource to the cache,
the browser checks the validity of the URL and the icon itself.
In cases of expired URLs (e.g., a 404 or 505 HTTP error
is raised) or non-valid icon les (e.g., a le that cannot be
rendered) the browser rejects the icon and no new entry is
created or modied. This ensures the integrity of the cache and
protects it from potential networking and connection errors.
Modify & Delete Entry
. If the browser nds the entry
in the cache, it checks the TTL to verify the freshness of
the resource. If it has not expired, the browser compares the
retrieved favicon ID with the one included in the header.
If the latter does not match the already stored ID (e.g.,
rel=``/fav_v2.ico"
) it issues a request and updates the
entry if the fetch succeeds. This process is also repeated if the
TTL has expired. If none of these issues occur, the favicon is
retrieved from the local database.
Access Control and Removal
. The browser maintains a
different instance of the F-Cache for each user (i.e., browser
account/prole) and the only way to delete the entries for a
specic website is through a hard reset [33]. Common browser
menu options to clear the browser's cache/cookies/history do
not affect the favicon cache, nor does restarting or exiting
the browser. Surprisingly, for performance and optimization
reasons this cache is also used when the user is browsing
in incognito mode. As opposed to other types of cached and
stored resources which are completely isolated when in incog-
nito mode for obvious privacy reasons [85], browsers only
partially isolate the favicon cache. Specically, the browser
will access and use existing cached favicons (i.e., there is
read permission in incognito mode), but it will not store any
new entries (i.e., there is no write permission). As a result,
the attack that we demonstrate allows websites to re-identify
any incognito user that has visited them even once in normal
browsing mode.
3

--- page 4 ---

0 5 10 15 20 25 30UNDEF173090180365Favicons (%)Expiration (days)

--- page 5 ---

Favicon use in the wild.
To better understand how favicons
are used in practice, we conduct a crawl in the Alexa [12]
top 10K using the Selenium automation framework [72], with
Chrome. Since some domains are associated with multiple
subdomains that might not be owned by the same organization
or entity (e.g, wordpress.com, blogspot.com) we also explore
how favicon use changes across subdomains. As such, for each
website, we perform a DNS lookup to discover its subdomains
using the
c
tool, and also visit the rst 100 links encountered
while crawling the website. Subsequently, we visit all collected
URLs and log the HTTP requests and responses as well as
any changes in the browser's favicon cache. We nd that 94%
of the domains (i.e., eTLD+1) have valid favicon resources,
which is an expected branding strategy from popular websites.
Next, we use an image hashing algorithm [41] to measure
how often websites deploy different favicons across different
parts and paths of their domain. We nd that 20% of the
websites actually serve different favicons across their subdo-
mains. While different subdomains may belong to different
entities and, thus, different brands, the vast majority of cases
are due to websites customizing their favicons according to the
content and purpose of a specic part of their website. Figure 1
reports the expiration values of the collected favicons. As
expected, favicon-caching expiration dates vary considerably.
Specically, 9% of the favicons expire in less than a day, while
18% expire within 1 to 3 months, and 22% have the maximum
expiration of a year. Finally, for

27% of the favicons a
cache-control directive is not provided, resulting in the default
expiration date (typically 6 hours) of the browser being used.
A. Threat Model
Our research details a novel technique for tracking users
by creating a unique browser identier that is “translated”
into a unique combination of entries in the browser's favicon
cache. These entries are created through a series of controlled
redirections within the attacker's website. As such, in our work
the adversary is any website that a user may visit that wants
to re-identify the user when normal identiers (e.g., cookies)
are not present. Furthermore, while we discuss a variation of
our attack that works even when JavaScript is disabled, we
will assume that the user has JavaScript enabled since we also
present a series of optimizations that signicantly enhance the
performance and practicality of our attack by leveraging robust
browser-ngerprinting attributes (which require JavaScript).
III. M
ETHODOLOGY
In this section, we provide details on the design and
implementation of our favicon-based tracking attack.
Overview & Design
. Our goal is to generate and store a
unique persistent identier in the user's browser. At a high
level, the favicon cache-based attack is conceptually similar to
the HSTS supercookie attack [78], in that full values cannot
be directly stored, but rather individual bits can be stored and
retrieved by respectively setting and testing for the presence
of a given cache entry. We take advantage of the browser's
favicon caching behavior as detailed in the previous section,
where different favicons are associated with different domains
or paths of a base domain to associate the unique persistent
identier to an individual browser. We express a binary numberAlgorithm 1:
Server side process for writing/reading IDs.
This process runs independently for each browser visit.Input:
HTTPS trafc logged in web server.
Output:
ID of visited browser.
IDVector =[N* 1]
// init N-bit vector
readmode=writemode=False
if
Request== GET : main page
thenif
NextRequest == GET : favicon.ico
thenwritemode= Trueelsereadmode= Trueif
writemode==True
then/
*
Write Mode
*
/
IDVector =GenerateID
// ID Bits mapping to Subpaths
RedirectionChain = Map [IDVector]
foreach
path in RedirectionChain
doRedirectBrowser (path)
waitForRedirection()
if
Request == GET : faviconX.ico
then// Write Bit
Response =
faviconX.icoelse if
readmode==True
then/
*
Read Mode
*
/
foreach
path in AllPaths()
doRedirectBrowser (path)
waitForRedirection()
if
Request == GET : faviconX.ico
then// Log the absence of the Bit
IDVector[path]=0
Response = [
404 Error
]return
IDVector(the ID) as a set of subpaths, where each bit represents
a specic path for the base domain, e.g.,
domain.com/A
corresponds to the rst bit of the ID,
domain.com/B
to the
second bit, etc. Depending on the attackers' needs in terms of
scale (i.e., size of user base) the number of inner paths can be
congured for the appropriate ID length. While the techniques
that we detail next can also be implemented using subdomains,
our prototype uses subpaths (we have experimentally veried
that the two redirection approaches do not present any dis-
cernible differences in terms of performance).
Following this general principle, we rst translate the
binary vector into subpaths, such that every path represents
a bit in the N-bit vector. For example, assume that we
generate an arbitrary 4-bit ID as a vector:
ID
=
<
0101
>
.
This vector has to be translated into a sequence of available
paths, which requires us to dene a specic ordering (i.e.,
sequence) of subpaths:
P
=
<
A, B, C, D
>
. The mapping is
then straightforward, with the rst index of
ID
- the most
signicant bit in the binary representation - mapped to the
rst subpath in
P
. This one-to-one mapping has to remain
consistent even if the attacker decides to increase the length
of possible identiers in the future, as doing so will allow the
site to accommodate for more users (by appending additional
subpaths in the
P
vector).
4

--- page 6 ---

Fig. 2: Writing the identier.
The next step is to ensure that the information carried by
the identier is “injected” into the browser's favicon cache.
The key observation is that each path creates a unique entry in
the browser favicon cache
if
it serves a
different
favicon than
the main page. As such, we congure different favicons and
assign them to the corresponding paths. Each path has its own
favicon congured in the header of its HTML page, which
is fetched and cached once the browser visits that page. The
presence of a favicon entry for a given path denotes a value
of
1
in the identier while the lack of a favicon denotes a
0
.
To store the ID, a victim needs only to visit the paths
f
B,D
g
, which results in storing
faviconB.ico
and
faviconD.ico
(the customized favicons of each paths). In the visits, the user
will be redirected through
all
subpaths. Since they have already
visited the sub-pages (B, D), their favicons are stored in the
browser's cache and will not be requested from the server. For
the remaining domains (A, C) the browser will request their
favicons. Here we take advantage of the browsers' caching
policies, and serve invalid favicons; this results in no changes
being made to the cache for the entire base domain and the
stored identier will remain unchanged.
In other words, our cache-based tracking attack is non-
destructive and can be successfully repeated in all subse-
quent user visits. Finally, a core aspect of the attack is the
redirection threshold
, which denes the time needed
for the browser to visit the page, request the favicon, store it
into the cache and proceed to the next subpath. A high-level
overview of our proposed methodology is given in Algorithm 1
and is further detailed in the next subsections.
A. Write Mode: Identier Generation & Storage
In the write mode, our goal is to rst make sure that
the victim has never visited the website before and to then
generate and store a unique identier. Since we control both
the website and the server, we are able to control and track
which subpaths are visited as well as the presence or absence of
specic favicons by observing the HTTP requests received by
the server. The succession of requests during the write mode
is illustrated in Figure 2. The rst check is to see whether
the favicon for the base domain is requested by the server
when the user visits the page. If that favicon is requested, then
this is the user's rst visit and our system continues in write
mode. Otherwise it switches to read mode. Next, we generate
a new N-bit ID that maps to a specic path
Redirection Chain
.
Fig. 3: Reading the identier.
Specically, we create a sequence of consecutive redirections
through any subpaths that correspond to bits with a value of
1
, while skipping all subpaths that correspond to
0
. Each path
is a different page with its own HTML le and each HTML
page contains a valid and unique favicon.
The redirection chain is transformed to a query string
and passed as a URL parameter. Each HTML page, then,
includes JavaScript code that parses the URL parameter and
performs the actual redirection after a short timing redirection
threshold (
waitForRedirection()
in Algorithm 1). The
redirection is straightforward to execute by changing the
window.location.href
attribute. For instance, for the ID
0101
we create the RedirectionChain= [B
!
D] and the server
will generate the query
domain?id=bd
. Finally, when the
server is in write mode it responds normally to all the requests
and properly serves the content. Once the redirection process
completes, the ID will be stored in the browser's favicon cache.
B. Read Mode: Retrieve Browser Identier
The second phase of the attack is the reconstruction of the
browser's ID upon subsequent user visits. The various requests
that are issued during the read mode are shown in Figure 3.
First, if the server sees a request for the base domain without
a corresponding request for its favicon, the server reverts to
read mode behavior since this is a recurring user. When the
server is in read mode, it
does not
respond to
any
favicon
request (it raises a
404 Error
), but responds normally to all
other requests. This ensures the integrity of the cached favicons
during the read process, as no new F-Cache entry is created
nor are existing entries modied.
In practice, to reconstruct the ID we need to force the user's
browser to visit all the available subpaths, and capture the
generated requests. This is again possible since we control the
website and can force redirections to all available subpaths
in the RedirectionChain through JavaScript. Contrary to the
write mode, here the set of redirections contains all possible
paths. In our example we would reconstruct the 4-Bit ID by
following the full redirection chain [A
!
B
!
C
!
D].
In the nal step, the server logs all the requests issued by
the browser; every request to a subpath that is not accompanied
by a favicon request indicates that the browser has visited this
page in the past since the favicon is already in the F-Cache, and
we encode this subpath as
1
. The other subpaths are encoded as
0
to capture the absence of this icon from the cache. Following
5

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the running example where the ID is
0101
, the browser will
issue the following requests:
[GET /A, GET /faviconA, GET /B, GET /C, GET /favi-
conC, GET /D]
. Notice here that for two paths we do not
observe any requests (info bit:
1
) while there are requests for
the rst and third path (info bit:
0
).
Concurrent users.
Since any website can attract multiple
concurrent users, some of which may be behind the same IP
address (e.g., due to NAT) in the rst step when the user visits
the website, we set a temporary “session” cookie that allows
us to group together all incoming requests on the server that
originate from the specic browser. It's important to note that
our attack is not affected by the user clearing their cookies
before and/or after this session (or are browsing in incognito
mode) since this cookie is only needed for associating browser
requests in this specic session. Furthermore, since this is a
rst-party session cookie it is not blocked by browsers' and
extensions' anti-tracking defenses.
C. Scalability
Dynamic identier lengths.
As each subpath redirection
increases the duration of the attack, websites can reduce the
overall overhead by dynamically increasing the length of the
N-bit identier whenever a new user arrives and all possible
identier combinations (
2
N
) for the current length have already
been assigned. This is trivially done by appending a new
subpath in the sequence of subpaths and appending a “0” at
the end of all existing user identiers. In our running example,
if the server goes from 4-bit identiers to 5-bit identiers,
the subpath vector will become
P
=
<
A, B, C, D, E
>
and
the identier
0101
will become
01010
, without any other
changes necessary. This results in the website only using the
minimum number of redirections necessary. While there is no
inherent limitation to the maximum length of our identier, we
consider 32 bits suitable even for the most popular websites
since 32 bits allow for almost 4.3 Billion unique identiers.
D. Selective Identier Reconstruction
As already discussed, our attack is not dependent on any
stateful browser information or user activity, but only leverages
the data stored in F-Cache. In general, the process of writing
and reading the unique tracking identier can be considered
costly due to the page redirections that are performed. Espe-
cially the read phase which reconstructs the ID by redirecting
through the full subpath sequence chain should only take place
when necessary and not upon every user visit, i.e., when
no other stateful browser identier is available. This can be
easily addressed by the use of a typical cookie that stores an
identier. This way, the website only needs to reconstruct the
tracking identier when the original request to the main page
does not contain this cookie (e.g., because the user cleared
all their cookies or is in incognito mode) thus removing any
unnecessary overhead.
E. Vulnerable Browsers
We perform a series of preliminary experiments to identify
which browsers are affected by our attack, and select the
most popular browsers and major operating systems. For these
experiments we visit our own attack website multiple times for
TABLE II: Compatibility of the attack across different plat-
forms and browsers. Combinations that do not exist are marked
as N/A. Browser Windows macOS Linux Android iOSChrome (v. 86.0)
3
3
3
3
3
Safari (v. 14.0) N/A
3
N/A N/A
3
Edge (v. 87.0)
3
3
N/A
3
N/A
Brave (v. 1.14.0)
3 3 3 3 3 TABLE III: Attack effectiveness under different scenarios:
when the user is browsing in private mode (
Incognito
), after
clearing the browser's user data (
Clear Data
), after installing
anti-tracking extensions (
Anti-Tracking
), and using a
VPN
.Browser Incognito Clear Data Anti-Tracking VPNChrome
3
3
3
3
Safari
3 3 3 3
Edge
3
3
3
3
Brave
3 3 3 3each browser and OS combination and monitor the requests
issued by the browser as well as the entries created in the
favicon cache so as to identify potential inconsistencies.
Table II presents the browsers that we found to be suscepti-
ble to our attack. In more detail, our attack is applicable on all
platform and browser combinations where the favicon cache is
actually used by the browser (we detail a bug in Firefox next).
Chrome, by far the most popular and widely used browser, is
vulnerable to our attack on all the supported operating systems
that we tested. We also identied the same behavior for Brave
and Edge, which is expected as they are both Chromium-
based and, thus, share the same browser engine for basic
functionalities and caching policies. We note that since the
F-Cache policies tend to be similar across different browser
vendors, the attack is most likely feasible in other browsers
that we have not tested.
Next, we also experimentally investigate whether our attack
is affected by normal defensive actions employed by users.
Specically, we explore the effect of re-visiting a website
in incognito mode, clearing the browser's user data (e.g.,
using the “Clearing Browsing Data” setting in Chrome) and
installing popular anti-tracking and anti-ngerprinting exten-
sions. As can be seen in Table III, the attack works against
users in incognito mode in all the tested browsers as they all
read the favicon cache even in private browsing mode (most
likely for performance optimization reasons). Similarly, we
nd that the option for clearing the user's browsing data has
no effect on the attack as the favicon cache is not included
in the local storages that browsers clear. Moreover, we nd
that installing popular privacy extensions that are available in
most platforms (i.e., Ghostery, UBlock, Privacy Badger
1
) does
not protect users from our attack, which is expected since our
attack presents the rst privacy-invasive misuse of favicons.
Finally, we also verify that if the user visits the website using
a VPN the attack is still effective, as the user's IP address does
not affect the favicon cache.1
Not available for Safari.
6

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Firefox
. As part of our experiments we also test Firefox.
Interestingly, while the developer documentation and source
code include functionality intended for favicon caching [27]
similar to the other browsers, we identify inconsistencies in
its actual usage. In fact, while monitoring the browser during
the attack's execution we observe that it has a valid favicon
cache which creates appropriate entries for every visited page
with the corresponding favicons. However, it
never
actually
uses the cache to fetch the entries. As a result, Firefox actually
issues requests to re-fetch favicons that are already present in
the cache. We have reported this bug to the Mozilla team,
who veried and acknowledged it. At the time of submission,
this remains an open issue. Nonetheless, we believe that once
this bug is xed our attack will work in Firefox, unless they
also deploy countermeasures to mitigate our attack (we provide
more details on our attack's disclosure in §VII).
IV. A
TTACK
O
PTIMIZATION
S
TRATEGIES
In this section we propose different strategies that can be
applied to improve our attack's performance without affecting
accuracy or consistency.
A. Weak devices: Identier Assignment Strategy
Our rst strategy is straightforward and aims to reduce the
overhead of the write phase (i.e., storing the identier) on a
per-client basis. Specically, our goal is to assign identiers
that require fewer redirections (i.e., have fewer
1
s) to resource-
constrained devices. While this approach does not provide
an optimization for the website at an aggregate level, since
all identiers for a given number of bits will be assigned to
users, it allows the website to selectively/preferentially assign
“better” identiers to devices with computational constraints
(e.g., smartphones) or devices that connect over high-latency
networks (e.g., cellular) to reduce the redirections. Currently,
websites can leverage the User-agent header for this, e.g., to
infer if users are on mobile devices or have an older browser
version. However, an experimental browser feature designed to
optimize content selection and delivery, the Network Informa-
tion API, is currently supported by several major browsers [6]),
allowing websites to also decide based on the nature of the
device's connection (e.g., if it is over a cellular network).
For this process, we need an algorithm for sorting IDs
that creates a different arrangement of the
1
bits in an ID -
the bits that are written through redirection- and assigns them
accordingly. In the vanilla version of our attack, for each new
client, we simply assign the next available binary identier
based on the number of identiers assigned so far and increase
the identier's length when necessary. This assignment follows
a simple decimal system enumeration, where the sequence of
values follows a simple progression:
X
=
[
01,10,11,100,101,110,111,1000,...
]
As such the ID represents the “arrival” order of each user's
initial visit to the website. To put it simply, the rst user
is assigned the ID=
01
, the second ID=
10
, and so on. To
optimize our ID assignment strategy we use a sorting heuristic.
Having a constant number of bits in the ID, the “ascending”
algorithm permutes the standard binary IDs and sorts them by
the total number of
1
s. This results in generating the same set
of IDs but in a different sequence. When new users visit the
website, constrained devices will be assigned the next available
identier from the top of the sequence (i.e., with fewer
1
s)
while more powerful devices or on high-speed networks are
assigned from the bottom of the sequence (i.e., with more
1
s.)
As we show in §V this approach can reduce the duration of
the write phase for constrained devices, especially for websites
with larger user bases that require longer identiers.
B. Adaptive Redirection Threshold
While our previous optimization focuses on the write mode
for weak devices and involves the internals of our attack, here
we outline a different technique that optimizes the attack's
overall performance. As dened in §III, the timing threshold
between the visits of each path is directly connected to the
attack's duration. Selecting this threshold is, thus, crucial since
an unnecessarily large value (e.g., 1 second) will greatly affect
the attack's stealthiness and practicality. On the other hand, if
the redirection threshold is too low (e.g., 10 ms), there will be
insufcient time for the browser to issue the request, receive a
response from the server, and store the favicon. Various factors
and constraints can affect the optimal threshold for a specic
user, including the user's browser, network connection, and
device characteristics. For instance, the attack should adopt a
higher threshold for mobile devices on a cellular connection,
compared to a desktop connecting from a residential network.
Furthermore, as we extensively explore in §V, the attack can
be further optimized by setting a lower threshold for clients in
the same geographic region or network.
C. Leveraging Immutable Browser Fingerprints
Moving a step further, we outline another method that
optimizes the attack's overall performance for
all
users. For
this, we rely on our following key observation: while browser
ngerprinting techniques do not typically provide sufcient
discriminatory information to uniquely identify a single device
at an Internet scale, they can be used to augment other track-
ing techniques by subsidizing part of the tracking identier.
Numerous studies have demonstrated various ngerprinting
techniques for constructing a persistent identier based on a set
of browser and system attributes [82], [50], [17], [32], [71].
These attributes are commonly collected through JavaScript
APIs and HTTP headers and form a set of system char-
acteristics that vary across different browsers, devices and
operating systems. Each of these features encodes different
types of information, and Shannon's notion of entropy can be
used to quantify the discriminatory power of the information
that they carry (as bits of entropy). Intuitively, higher levels
of measured entropy denote more information being stored
in the variable. When focusing on ngerprinting attributes,
high entropy represents features that encode information about
larger spaces of potential values, while lower entropy is
found in the features with smaller value ranges. For instance,
features that store binary information (
Cookies Enabled,
Use of Local Storage
) have lower entropy values in
comparison to attributes that encode a wider range of values
(e.g.,
Platform/OS, WebGL metadata
).
However, one crucial characteristic of browser ngerprints
is that certain ngerprinting attributes are volatile and fre-
quently change, thus reducing their suitability for long-term
tracking. Indeed, Vastel et al. [82] found that features with
7

--- page 9 ---

Fig. 4: Ordering of robust ngerprinting attributes and corresponding values from our real-world ngerprinting dataset (See §V).
The “
*
” refers to the remaining attributes (FP) and which are not visualized here.
higher entropy, like the display resolution, timezone, browser
fonts, plugins, are more likely to change due to common user
behavior. Such examples can be users that travel a lot (different
timezones) or install/disable plugins based on their needs.
These changes are reected in the attributes, thus, altering the
browser ngerprint over time. To overcome this obstacle and
enable long-term tracking, our strategy is to use a set of robust
features that remain immutable over time, and use them as
part of our tracking identier. Table IV presents the browser
attributes that rarely change along with the measured values
of entropy, and the total entropy of those values accumulated,
as reported by prior studies in the area. The reported entropy
values vary as each study recruited different types and numbers
of users (e.g., one study involves privacy-aware users), and
implemented different approaches to collect those data.
Nonetheless, the general properties of each attribute remain
consistent, e.g., binary attributes have the lowest entropy.
Moreover, as shown in prior work [82] the rst 4 attributes are
constant over time, while the remaining 6 rarely change for a
small amount of users (

10%) over a one-year period. This is
expected considering the fact that if the Platform or any of the
WebGL features alter, in essence the device becomes different
and cannot be treated as the same browsing instance. Moreover,
users' browsing preferences, like disabling an AdBlocker [50],
[22], [32] or accepting cookies are unlikely to change.
We use these robust attributes and the numbers reported
in representative prior work to calculate the total entropy of
these features in bits, which we will use to create a K-bit
immutable identier that will be used to subsidize K bits
from our favicon-based identier, thus reducing the number
of redirections required during our attack. Based on Table IV,
the entropy that we can obtain from these robust attributes
varies between 21-26 bits. While this approach adds a new
layer of complexity to the attack, it signicantly optimizes the
performance of our attack as we demonstrate in §III.
Combining favicons and ngerprints.
Having identied
that browser attributes can be used to decrease the favicon
identier's size, we further investigate this strategy and provide
a concrete methodology. In more detail, each attribute encodes
information that is common with a number of other devices
which results in the formation of anonymity sets, i.e., multiple
devices with the same ngerprint. In our case, where we use a
subset of the 17 attributes that are usually collected to form a
TABLE IV: Persistent browser attributes and their entropy
reported in the AmIUnique [50], (Cross-)Browser ngerprint-
ing [17] and Hiding in the Crowd [32] studies. Attribute AmIUnique Cross-Browser CrowdCookies Enabled
0.25
0.00
0.00
Local Storage 0.40 0.03 0.04
Do Not Track
0.94
0.47
1.19
Ad Blocker 0.99 0.67 0.04
Platform
2.31
2.22
1.20
Content Encoding 1.53 0.33 0.39
Content Language
5.91
4.28
2.71
WebGL Vendor 2.14 2.22 2.28
WebGL Renderer
3.40
5.70
5.54
Canvas 8.27 5.71 8.54Total
26.14
21.63
21.93 ngerprint, the chance of creating a signature that is not unique
is higher. Also, it is important to note that the collection of
certain device attributes may be blocked by privacy-oriented
browser extensions or even as part of a browser's normal
operation (e.g., in Brave).
This necessitates a methodology for generating identiers
that dynamically decides how many identier bits will be
obtained from a specic browser's ngerprints based on their
availability and discriminating power (i.e., their entropy as cal-
culated for a website's aggregate user base). More concretely,
we dene the following:
 V
: set of browser attributes.
 W
: distribution of values of vector
V
.
 FP
ID
: ngerprint-based ID with a length of
K
bits.
 FV
ID
: favicon-based ID with a length of
J
bits.
 T
ID
: unique tracking ID with a length of
N
bits.
In general, we assume that each attribute in
V
has a range
or set of possible values that are not uniformly distributed.
For instance, in our dataset (described in §V) most users are
actually on a Linux platform and, of those, the vast majority
(

80%) has a specic WebGL Vendor. These frequency
distributions are expressed as a normalized weight
W
that
captures the portion of the data that each value has, over the
entire set of possible values. While in our analysis we use
8

--- page 10 ---

 "'$
..!

--- page 11 ---

(a) Desktop
(b) Mobile Device
Fig. 5: Favicon-caching outcome for different redirection thresholds.
per-attribute entropy calculations based on prior studies and
a real-world dataset, we assume that individual websites will
tweak those values based on their own user base allowing
them to more accurately infer
H
. Since set
V
may not contain
all ten browser attributes for certain users, its measured
entropy
H
will vary based on the availability of attributes.
Taking all these variables into consideration, we dene the
following relationships:
H
(
V
;
W
)
! FP
ID
FP
ID
++
FV
ID
! T
ID
Our proposed attack relies on the generation of the two differ-
ent identiers with a combined length of
N
, where
N
=[2,32]
depending on the size of each websites' user base.
In practice, the website will generate the unique tracking
ID
T
ID
as follows. First, the website will dene a standard
ordering of the attributes which are used as ngerprint inputs
– an example of conceptual visualization along with corre-
sponding attribute values is shown in Figure 4. When a new
user arrives, the website will retrieve all available attributes
Attr
i
2 V
and obtain their hashed representation. These
hashes are concatenated into a single string following the
aforementioned standard ordering, with any missing attributes
skipped, and then converted into a hash. Subsequently, the
website calculates the total discriminating power (i.e., entropy)
of the available attributes for that specic user and rounds that
down to the next whole bit to calculate
K
. Then it truncates
the hash to its
K
most signicant bits to create
FP
ID
which,
essentially, is a coarse identier that corresponds to the pool
of users that share that specic set of ngerprinting-attribute
values (i.e., an anonymity set). Finally, the website calculates
FV
ID
to match the next available to-be-assigned identier
T
ID
of length
N
, and stores the favicon entries that correspond
to
FV
ID
in the user's favicon cache as described in §III.
V. E
VALUATION
In this section we provide an experimental evaluation of our
attack that explores the practicality and performance of several
dimensions of our attack under different realistic scenarios, and
also measures the performance improvement obtained by our
optimization techniques.
A. Experimental Setup and Methodology
Server & Frameworks.
To perform our experiments we
rst deploy an attack website in the AWS Lightsail environ-
ment [11]. We use a dedicated Virtual Machine to minimize
potential overhead due to congested system resources. Speci-
cally, our server was built on top of a Quad Core Intel i7-7700
with 32GB of RAM. We also registered a domain name to
ensure that our measurements include the network latencies of
a realistic attack scenario (i.e., DNS lookup etc).
We opted to locate our VM and DNS zone in the same
geographical region with our user devices, to replicate a
reasonable scenario where the tracking website leverages a
geographically-distributed CDN infrastructure to minimize the
distance between their servers and users. However, since AWS
does not offer a hosting service in our own state, we select the
closest option (distance

350 miles) for our main experiments.
We implemented our website using the Python Flask
Framework [79], a popular and lightweight framework for
deploying web applications.The web application is congured
under an Nginx server [7] that acts as a reverse proxy and
load balancer, and communicates with the main application
and the browser. Our server runs on Ubuntu 18.04 LTS, using a
dedicated static IP address. To make the website accessible for
all the tested devices and frameworks, we registered an ofcial
domain with a valid HTTPS certicate. We believe that even
a modest website can recreate (or even signicantly augment)
this setup by deploying more powerful servers and different
combinations of web development tools and frameworks.
Clients.
We leveraged Selenium [72] to orchestrate
browsers that pose as desktop users that visit our attack
website. We used an off-the-shelf desktop with a 6-core Intel
Core i7-8700, 32GB of RAM, connected to our university's
network. Every experiment consists of the automated browser
visiting the attack website two distinct times so as to capture
both phases of the attack; in the rst visit the website generates
and stores the tracking identier (
write mode
), while in the
second visit it reconstructs it (
read mode
). For every phase we
measure the time required for the users' browser to complete
the chain of redirections through the base domains subpaths
and the server to write or read the identier. Since we do
not include any other resources on the website, the favicon
is fetched once the request for the main page completes
9

--- page 12 ---

406080100110120 0 20 40 60 80 100Threshold (ms)RunFailureSuccess

--- page 13 ---

4080120160180200 0 20 40 60 80 100Threshold (ms)RunFailureSuccess

--- page 14 ---

(a) Desktop
(b) Mobile Device
Fig. 6: Performance evaluation for the two stages of the attack for a desktop and mobile device.
successfully. For the mobile device experiments, we used a
low-end mobile device (Xiaomi Redmi Note 7) connected to
the cellular network of a major US provider. To automate the
experiments we use the Appium framework [28], which allows
the automation of both real and emulated mobile devices. All
the measurements that we present, consist of 500 repetitions
for each given conguration, unless stated otherwise.
B. Redirection Threshold Selection
First, we need to identify a suitable value for the threshold
between redirections, as too small a value can result in the
browser not fetching and caching the favicon while larger
values will unnecessarily increase the attack's duration. As
such, we experimentally explore different threshold values, as
shown in Figure 5, using both our desktop and mobile device
setups. Here we label a specic iteration as a
Success
if (i)
the browser visits the landing page and successfully requests
the favicon, (ii) the server issues a valid response, and (iii) the
browser stores the favicon and redirects to an inner path. If
any of those steps fail, we label this iteration as a
Failure
.
Our results show that a threshold of 110 ms is sufcient
for the browser to always successfully request and store the
favicon resource on the desktop device. Comparatively, an
increased redirection threshold of 180 ms is optimal for the
mobile device – this is expected due to differences in the
computational capabilities and network connections between
the two setups. We use these threshold values in the remainder
of the experiments, unless stated otherwise.
C. Attack Performance
Next we measure various aspects of our attack's perfor-
mance. For our experiments we use Chrome as it is the
most prevalent browser. First, we conduct 500 successive runs
of our attack for varying identier lengths between 2 and
32 bits – recall that websites can dynamically increase the
length to accommodate an increasing user base. The results
are illustrated in Figure 6.
Desktop browser
. The performance measurements for the
desktop browser are given in Figure 6a. Considering the nature
of the attack, the time required for the write phase is affected
by the number of
1
bits as that denotes the number of
redirections. This is clearly reected in the distribution of
execution times for each ID size, with the range of variance
also slightly increasing as the ID length increases. Nonetheless,
even for a 32-bit identier the median time needed to store
the identier is only 2.47 seconds. While the write phase
poses a one-time cost, since this is only needed the rst time
the user visits the website, our optimization techniques can
vastly improve performance. If the website leverages the user's
browser ngerprints, assuming 20 bits of entropy are available
(which is less than what has been reported by prior studies, as
shown in Table IV), then the 12 remaining identier bits can
be stored in the favicon cache in approximately one second.
Figure 6a also reports the experimental results for the read
phase for the complete range of ID sizes. As opposed to the
distribution of the write phase durations, here we see a very
narrow range of values for all ID sizes, where all measurements
fall right around the median value. This is expected as the
read phase requires that the user's browser traverses the entire
redirection chain, which is also apparent by the effect of the
ID size on the attack's duration. The minimum time needed to
read a 4-bit ID, is

1 second, and it proportionally grows as
the length of the ID increases. Considering again the scenario
where the website also leverages the browser ngerprints, the
attacker can reconstruct the unique tracking identier in less
than two seconds (median: 1.86 seconds).
Mobile browser
. The duration of the two phases when
using a mobile device is shown in Figure 6b. As one might
expect, there is an increase in the attack's duration for both
attack phases and all identier lengths, due to the reduced
computational power of the mobile device. As such, the im-
portance of the optimization techniques is even more necessary
for mobile devices – we further explore their effect in the next
subsection, and nd that for the mobile devices in our dataset
at least 18 bits of entropy are always available, which would
allow our attack to complete in

4 seconds.
10

--- page 15 ---

0 1 2 3 4 5 6 7 4 8 12 16 20 24 28 32Time (sec)ID size (bits)WriteRead

--- page 16 ---

0 2 4 6 8 10 12 14 4 8 12 16 20 24 28 32ID size (bits)WriteRead

--- page 17 ---

(a) ID Range : [4K, 65K]
(b) ID Range : [130K, 2M]
(c) ID Range : [4M, 250M]
Fig. 7: Total number of redirections for the two different ID generation algorithms, for the rst 250 Million identiers.
D. Optimization Effect: ID Assignment Algorithm
In §IV-A we presented an alternate ID generation algorithm
that creates an “optimized” sequence of identiers for a given
length N, by permuting the order of “1”s in the ID. The
goal is to assign better identiers to users behind resource-
constrained devices or slower connections. To quantify its
effect, we simulate an execution scenario where new users visit
the website and the number of ID bits increases accordingly. To
measure the effect of this optimization technique we compare
the total number of write bits generated when using the
two different identier-generation algorithms (Standard/As-
cending), especially for larger numbers of generated IDs. To
better quantify the benet for weaker devices, all devices are
assigned the next available identier in the sequence (i.e., for
the Ascending algorithm we always assign the next available
identier from the top of the sequence). We generate a variety
of IDs that range from 12 to 28 bits in length, in order to
capture the potential effect of the algorithm(s), under a realistic
number of users for both popular and less popular websites.
Figure 7 illustrates the total number of redirection bits for
250 Million IDs for different ID lengths. Even though the
number of IDs in each bin remains stable, since it represents
the users visiting the websites, we can clearly observe a
reduction in the total number of write bits used by the
ascending algorithm each time. Specically, for the rst set
of IDs (Figure 7a) the measured average decrease is

16%
across different sizes of IDs. Similarly, for the reported IDs in
the other ranges shown in Figures 7b, 7c, the total number of
redirection bits is reduced by

15%. Overall, the ascending
algorithm optimizes the ID when a new bit (subdomain)
is appended to the original schema and more permutations
of
1
become available. Compared to the standard approach,
this algorithm can considerably improve the attack's write
performance for weaker devices.
E. Optimization Effect: Leveraging Browser Fingerprints
Next we explore various aspects of our optimization that
relies on the presence of robust browser-ngerprinting at-
tributes. As detailed in §IV-C, retrieving such attributes and
computing their entropy allows us to effectively reduce the
required length of the favicon-based identier. Due to the
default behavior of certain browsers or the potential presence
of anti-tracking tools, the availability of each attribute is not
uniform across browsers. Furthermore, different combinations
of available browser attributes will result in anonymity crowds
of varying sizes. As such we conduct an analysis using a real-
world ngerprinting dataset.
We contacted the authors of [50] who provided us with
a dataset containing real browser ngerprints collected from
amiunique.org
during March-April 2020. To measure the
distribution of the various ngerprinting attributes and their
values across different browsers, we lter the dataset and only
store instances that have available data for the immutable
features in Table IV. In more detail, we reject any entries where
all attributes are either empty or obfuscated. We consider as a
valid ngerprint any entry that has a stored value for at least
one of the attributes. For example, entries that only contain a
Platform attribute will be kept, even if the remaining attributes
are unavailable or obfuscated. This leaves us with 272,608
(92.7%) entries, which we consider in our subsequent analysis.
The removed entries also include platforms that are found
infrequently (e.g., Smart TVs, gaming consoles).
Since we do not know a-priori which features are available
for each device, we conduct a more in-depth analysis of the
dataset and measure the availability of browser ngerprints
and the sizes of the anonymity sets that they form. For each
device, we read each attribute, and based on the corresponding
entropy we sum the entropy values for the available set of
immutable features. We nd that in this dataset the attributes
that were most commonly unavailable due to obfuscation were
the WebGL metadata; however, this occurred in

0.05% of
the ngerprinting instances indicating that the effect of missing
ngerprints would be negligible in practice.
A break down of our results for the various platforms is
given in Figure 8. For desktop platforms, the lowest measured
entropy from available attributes is 16 bits, revealing that most
immutable attributes are always available. Interestingly, for
more than half of the devices running Windows we gain 24
bits of entropy, while comparatively Linux and MacOS devices
expose 19 bits. These numbers demonstrate that leveraging
traditional ngerprinting attributes provides a signicant per-
formance optimization as a website would only need between
6-14 favicon-based identier-bits for

99.99% of the devices.
We can also see that for iOS devices approximately 90% of the
devices provide a little over 18-bits of entropy, while Android
devices tend to expose attributes with more discriminating
power resulting in half the devices having more than 21 bit
of entropy. As such, in practice, while the attack's duration
can be signicantly reduced for all types of mobile devices,
Android devices present increased optimization benets.
11

--- page 18 ---

0*10
01*10
52*10
53*10
54*10
55*10
54K8K16K32K65KRedirection BitsGenerated IDs
StandardAscending

--- page 19 ---

0*10
06*10
61*10
72*10
72*10
73*10
7130K260K500K1M2MGenerated IDs
StandardAscending

--- page 20 ---

0*10
03*10
86*10
89*10
81*10
92*10
94M8M16M67M250MGenerated IDs
StandardAscending

--- page 21 ---

Fig. 8: Fingerprint size for desktop and mobile devices.
TABLE V: Time required for reading each ngeprinting at-
tribute, and amount of time saved due to the reduction in the
number of necessary redirections. Time Spent (ms)
Time Saved (ms)
mean (

) stdev (

)Cookies
2.83
2.86
0
Storage 2.56 2.99 0
DNT
0.32
0.95
110
Ad Blocker 8.21 8.29 110
Platform
0.20
0.08
220
HTTP Metadata 0.42 0.60 770
WebGL Metadata
74.21
13.22
550
Canvas 105.96 11.64 880
All
200.19
20.23
1,760 Attribute effect.
Next, we conduct a more in-depth explo-
ration of these benets by conducting a cost-benet analysis of
each ngerprinting attribute by measuring the time required to
obtain it and the corresponding improvement of the provided
entropy, shown in Table V. In more detail, attributes that are
retrieved through the
Navigator
object (Cookies Enabled,
Storage) can be retrieved almost instantaneously, whereas more
complex attributes like Canvas and WebGL need at least 100
ms to be processed. The retrieval of each attribute, depending
on its internal properties and the gained entropy, decreases
the required length of the favicon-based identier and, thus,
the redirection time needed for reading the browser ID. For
example, the existence of the DNT attribute provides almost 1
bit of entropy which saves 1 identier bit (i.e., one redirection)
resulting in a 31-bit
FV
ID
. Similarly, the HTTP-metadata
provide 7 bits of information, thus needing 7 fewer redirections
(840 ms); this would optimize the total attack performance
by 22%. Obtaining all the aforementioned attributes from
a specic browser instance, requires 200ms. If we add this
overhead to the duration of the favicon attack reported in
Figure 6a for 12-bit identiers, we nd that in practice our
optimized attack requires

2 seconds for reconstructing a 32-
bit tracking identier when 20 bits of entropy are available.
This can be further optimized using the adaptive threshold.
TABLE VI: Popular anti-ngerprinting tools, their defense
technique, and the number of entropy bits available from
ngerprinting attributes when they are present in the user's
browser. Here

denotes that access to the attributes is blocked
by the tool and
	
that the attribute values are randomized. Users Strategy
Remaining
Entropy (bits)CanvasFingerprintBlock [13]
5K

18
Canvas Fingerprint Defender [87] 10K
	
18
Canvas Blocker [42]
9K

18
WebGL Fingerprint Defender [45] 4K

21
Brave browser [15]
8M
 

12 Anti-ngerprinting defenses.
Next, we explore the im-
plications of users leveraging anti-ngerprinting defenses. For
both the desktop and mobile datasets we observe a high
availability of ngerprinting attributes, indicating that such
defenses are not commonly deployed. In practice, this could
be partially inuenced by the nature of the dataset, which
originates from users of
amiunique.org
, as users may de-
cide to deactivate any anti-ngerprinting defenses when testing
the uniqueness of their system. However, recent work [22]
has found that only a small number of users employ such
privacy-preserving tools, which may also be ineffective in
practice. Specically, browser extensions that obfuscate and
randomize attributes such as the Platform, HTTP headers or
session storage, may fail to effectively mask the values. This
lack of widespread deployment is also due to the fact that
popular anti-tracking tools (e.g., AdBlock, Ghostery, uBlock,
Privacy Badger) focus on detecting and blocking 3rd-party
domains that are potentially malicious or used by trackers and
do not actively defend against ngerprinting; as such we expect
a similar availability of ngerprints in practice.
Nonetheless, browsers like Brave have recently adopted
built-in anti-ngerprinting techniques which can affect our
attack's performance (while Tor has done so for years, we do
not consider it in our experiments since it is not susceptible to
our favicon attack). In more detail, Brave's documentation [15]
reports two different defenses against WebGL and Canvas
ngerprinting; the standard defense mode includes the random-
ization of certain ngerprinting attributes to avoid breaking
websites' functionality, while the strict mode blocks these API
calls which can potentially break website functionality. In our
analysis, we use Brave's strict mode.
To quantify the effect of such privacy-preserving mecha-
nisms on our attack's performance, which would stem from
missing ngerprinting attributes, we select the most popu-
lar extensions that defend Canvas and WebGL ngerprinting
from Google's web store, and the Brave browser. Table VI
reports the number of available entropy bits when each tool
(or browser) is used. Specically, we consider that if any
tool either randomizes or blocks a specic ngerprinting
API the corresponding attributes are unavailable. Interestingly,
we observe that none of the anti-ngerprinting extensions
affect the immutable attributes that we use for our attack
optimization. Out of the 26 bits of entropy that the website
could potentially obtain if the entire ngerprinting vector was
available, the Canvas-based defenses will end up removing 8
bits. The WebGL-based defense is less effective as 21 bits
12

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0 0.2 0.4 0.6 0.8 1 16 18 20 22 24 26CDFEntropy Bits
WindowsLinuxMacOSiOSAndroid

--- page 23 ---

(a) Safari
(b) Brave
Fig. 9: Attack performance for alternative browsers.
of entropy will still be available. Brave actually achieves the
highest reduction as only 12 bits are left. Nonetheless, even
in this case, reading the remaining 20-bits using our favicon-
based attack would require

3.1 seconds. Overall, while the
presence of anti-ngerprinting defenses could result in a less
optimized (i.e., slower) performance, our attack's duration
remains acceptable.
It is also important to note that while blocking a specic
ngerprinting call may be considered a stronger defense, in
this case it works in the favor of the attacker since they can
easily ignore that specic attribute. On the other hand, using a
randomized value will result in the website calculating differ-
ent identiers across visits. As such, websites can leverage
extension-ngerprinting techniques [71], [44], [77], [74] to
infer the presence of these extensions and ignore the affected
attributes when generating the
FP
ID
. For Brave, websites
simply need to check the User-agent header.
F. Evaluating Browser Performance
As shown in Table II, several browsers across different
operating systems are vulnerable to our attack. To explore
whether different browsers result in different attack durations,
we repeat our experiment with two additional browsers and
the user connected to a residential network, as illustrated in
Figure 9. Specically, we evaluate Safari as it is a popular
choice for MacOS users, and Brave as it is Chromium-based
and privacy-oriented. Surprisingly, while Brave's writing per-
formance is comparable to that of Chrome (Figure 6a), there is
a measurable increase when reading the identier (the median
attack for a 32-bit ID is 1.35 seconds slower than Chrome).
For Safari we observe that the attack's overall performance is
similar to Chrome and even slightly better for some ID sizes.
Our experiments show that differences in browser internals
can affect the performance of straightforward operations like
caching and reading favicons, even when powered by the same
engine (as is the case with Brave). As such, the benet of our
ngerprint-based optimization will be even more pronounced
for Brave users.
G. Evaluating Network Effects
To measure the effect that different network and infras-
tructure conditions can have on the attack's performance, we
conduct a series of experiments that explore alternative server
and client network setups.
Server location.
First, we aim to measure how our attack's
performance is affected for different web server locations.
For this set of experiments, we use our vanilla attack with
a consistent redirection threshold value of 110ms. We then
compare the attack's duration for a selection of identier
sizes, for three different locations, as shown in Figure 10.
SameCity
captures the scenario where the victim and web
server are located within the same city; since AWS does not
offer any hosting options in our area, we host the server in our
academic institution's computing infrastructure. The
StateA
scenario uses a server hosted on Amazon AWS in a different
geographic region (distance

850
miles), while the
StateB
experiment uses an AWS server located in a distant region
(distance

2
;
000
miles).
As one might expect, we nd that the attack requires less
time to complete when the server and client are located in
the same city. Specically, for a 32-bit ID size the median
value is

27% faster for writing the identier compared to
the other locations, while the reading time is decreased by
35% compared to the distant server
StateB
. This experiment
veries that there is a noticeable performance impact for
distant locations, however the attack maintains a practical
duration in all scenarios.
To further measure the effect of the server's location on
the performance, we repeat our
threshold selection
experiment
using the server deployed in our academic institution and the
desktop client connecting from a residential network in the
same city. Under these conditions, we nd that a redirection
threshold of 70 ms is sufcient for the browser to successfully
request and store the favicons, which signicantly reduces
the attack's overall duration; e.g., for a 32-bit identier the
median read and write values are 1.5 and 3.14 seconds re-
spectively. Overall, our experiments demonstrate that attackers
with access to a distributed infrastructure resources (which is a
13

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0 1 2 3 4 5 6 7 8 4 8 12 16 20 24 28 32Time (sec)ID size (bits)WriteRead

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(a) SameCity
(b) StateA
(c) StateB
Fig. 10: Attack performance evaluation for servers located in different regions.
Fig. 11: Performance evaluation for clients connecting from
two different networks: a high speed network in our academic
institution (
Acad.
) and a residential network (
Res.
).
reasonable assumption for modern attackers) can considerably
reduce the attack's duration by using CDNs and dedicated
machines across different locations.
Client network.
We explore how the attack's performance
changes depending on the type of the user's network. To that
end, we use the server deployed in our academic institution
(to reduce the effect of the server's location) and test two
different client network setups. In the rst case, we explore
a more ideal scenario where the user is connected to the
same (academic) network, while the second setup showcases a
scenario where the user is connected to a different (residential)
network. As shown in Figure 11, the performance is consistent
across networks for approximately half of the attack runs. For
smaller identier sizes there is no discernible difference during
the writing phase, while there is a small improvement in the
reading phase for approximately 25% of the attacks when the
client is on the academic network. Additionally, when the user
is on the residential network approximately 25% of the runs
exhibit a small increase in the attack's duration. For larger
identiers we see a higher variance in the write phase for the
user on the academic network, while we again observe that the
reading phase exhibits less variance when the user is on the
academic network. Overall, we do not observe a considerable
difference in the attack's performance even when the client is
on a residential network, further demonstrating the robustness
of using favicons as a tracking vector in real-world scenarios.
VI. M
ITIGATIONS AND
C
OUNTERMEASURES
Here we propose mechanisms for mitigating the tracking
vector enabled by favicon caching. We outline necessary
browser modications and potential countermeasures, and dis-
cuss the limitations they face in practice. Due to the nature of
the underlying browser functionality leveraged by our attack,
there is no straightforward countermeasure that can prevent the
attack without affecting the user's browsing experience.
Incognito mode.
Browsers currently allow the favicon
cache to be read even when the user is browsing in incognito
mode. While this is allowed for performance reasons, it is a
design aw in the browsers' isolation mechanism that should
be addressed. Similar to other cached content, a separate
isolated instance of the cache should be created for each
browsing session. Even if this will introduce a small additional
delay for new favicons that need to be fetched, we consider the
overhead reasonable as the underlying browser functionality is
not affected and there is an obvious privacy gain for users.
Cookie-tied favicon caching.
The main defense against
our attack is to remove the suitability of the favicon cache
as a tracking vector. Specically, by tying the use of cached
favicons to the presence of a rst-party cookie, the browser
can basically invalidate any additional benet of using the
favicon cache to track the user; if cookies are already present
then the website can obviously match the user to previous
browsing sessions. A straightforward way to implement this
defense is to simply clear the favicon cache whenever the user
deletes the cookie jar and other local storages and caches (e.g.,
through the “Clear browsing data” option in Chrome). The
downside of this countermeasure is the potential performance
penalty; if F-Cache entries are deleted frequently or after every
browser session, favicons will need to be re-fetched every time
users revisit each website. Nonetheless, this overhead is not
prohibitive even for cellular networks, since fetching a favicon
is an asynchronous and non-blocking operation.
Navigation-based favicon caching.
Browsers can poten-
tially employ an alternative strategy for preventing our attack,
where the caching of favicons is managed based on the
14

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navigation's transition type [3]. Specically, if a navigation
occurs to a different subpath or subdomain for which a F-
Cache entry does not exist, the browser will fetch the favicon
and create the entry
only
if the user initiated the navigation.
While this strategy does not introduce the (negligible) per-
formance overhead of the previous caching strategy, it could
potentially be bypassed if the website slowly recreates the
identier throughout the user's browsing session where each
click on a link is used to obtain one identier bit. Naturally,
such an attack strategy would face the risk of incomplete
identier reconstruction in short user sessions, and would
be more suitable for certain categories of websites (e.g., e-
commerce). We further discuss the attack strategy of stealthily
reconstructing the identier in §VII.
VII. D
ISCUSSION
Attack detection.
URL redirections have been proposed
in various prior studies as a signal for detecting malicious
activities (e.g., malware propagation [58], SEO poisoning [54],
click jacking [88]). However, in such cases the redirection
typically involves redirection to
different
domains or hosts,
which does not occur in our attack. Nonetheless, one could
potentially deploy a detection mechanism that also checks
intra-domain redirections. In such a case, a website could
potentially opt for a stealthier strategy where the chain of
redirections is completed in phases over the duration of a
user's browsing session. Based on statistics regarding average
user behavior within a given domain (e.g., session duration,
number of links clicked) a website could optimize this process
by creating partial redirection chains that are completed each
time a user clicks on a link or navigates within the website.
Especially for websites like social networks, search engines,
e-commerce and news websites, where common browsing
activity involves clicking on numerous links and visiting many
different pages, the website could trivially include one or two
additional redirections per click and avoid any redirection-
based detection. When taking into consideration our optimiza-
tion strategies, such a website could trivially reconstruct the
12-bit favicon-based identier without a considerable impact
on the attack's coverage (i.e., aggregate user stats would allow
the website to ne-tune a stealthy attack so only a very small
percentage of users terminates a browsing session without
completing the entire redirection chain).
Tracking and redirections in the wild.
A recent study on
the page latency introduced by third-party trackers for websites
that are popular in the US [36] reported that only 17% of
pages load within 5 seconds while nearly 60% and 18% of
pages require more than 10 and 30 seconds respectively. Their
analysis also highlighted the dominating effect that trackers
have on the overall page-loading latency, with an average
increase of 10 seconds. When taking their reported numbers
into consideration, it becomes apparent that the cost of our
attack is practical for real-world deployment. Furthermore,
Koop et al. [47] just recently studied how redirections to
third-party trackers are commonly employed as a means for
them to “drop” tracking cookies in users' browsers. As such,
the underlying mechanism that drives our attack, resembles
behavior that is already employed by websites, reducing the
likelihood of our attack being perceived as abnormal behavior.
Deception and enhancing stealthiness.
While redirections
are already part of “normal” website behavior and, thus, may
not be perceived as concerning or malicious by average users,
several deceptive strategies can be employed to further enhance
the stealthiness of our attack. For instance, websites can em-
ploy various mechanisms for distracting the user (e.g., a popup
about GDPR compliance and cookie-related policies [81]).
Additionally, JavaScript allows for animations and emoticons
to be encoded in the URL [67]. An attacker could use such
animated URL transitions to obscure the redirections. Finally,
Chrome is currently experimenting with hiding the full URL
in the address bar and only showing the domain [4] as a way
to combat phishing attacks [68]. If Chrome or other browsers
permanently adopt this feature where only the main domain
is shown by default, our attack will be completely invisible to
users as it leverages redirections within the same domain.
Anti-ngerprinting.
Our work presents an attack which,
conceptually, is a member of the side-channel class of attacks.
One important implication of our work, with respect to browser
ngerprinting and online tracking, is that such an attack
composes well with any number of entropy bits available from
traditional browser ngerprinting; while browsers are working
to decrease the aggregate entropy down to prevent unique
device identication, e.g., Brave [15], the remaining bits are
still incredibly powerful when composed with a side-channel
tracking technique.
In more detail, while even the vanilla version of our attack
is well within the range of overhead introduced by trackers
in the wild [36], leveraging immutable browser-ngerprinting
attributes signicantly reduces the duration of the attack.
As such, while browser ngerprints typically do not possess
sufcient discriminating power to uniquely identify a device,
they introduce a powerful augmentation factor for any high-
latency or low-bandwidth tracking vector. Furthermore, while
our attack remains feasible even without them, other tracking
techniques may only be feasible in conjunction with browser
ngerprints. As such, we argue that to prevent the privacy
impact of as-yet-undiscovered side-channel attacks and track-
ing vectors, anti-ngerprinting extensions and browser vendors
should expand their defenses to include
all
the immutable
ngerprinting attributes we leverage in our work instead of
focusing on a single (or small set) of attributes.
Favicon caching and performance
. Recently certain
browsers have started supporting the use of
Data URI
s for
favicons. Even though this technique can effectively serve and
cache the favicon in the user's browser almost instantaneously,
it cannot currently be used to optimize our attack's perfor-
mance. In more detail, the write phase does not work since
the browser creates different cache entries for the base-64 rep-
resentations of the favicons, and stores a different sequence of
icons than those served by the page. Moreover, since there are
not requests issued by the browser for such resources, reading
those cached-favicons would not be possible. Finally, we also
experimented with the HTTP2 Server Push mechanism, but
did not detect any performance benet.
Ethics and disclosure.
First we note that all of our
experiments were conducted using our own devices and no
users were actually affected by our experiments. Furthermore,
due to the severe privacy implications of our ndings we
have disclosed our research to all the browser vendors. We
submitted detailed reports outlining our techniques, and ven-
15

--- page 32 ---

dors have conrmed the attack and are currently working on
potential mitigations. In fact, among other mitigation efforts,
Brave's team initially proposed an approach of deleting the
Favicon-Cache in every typical “Clear History” user action,
which matches our “Cookie-tied favicon caching” (see §VI)
mitigation strategy that can work for all the browsers. The
countermeasure that was eventually deployed adopts this ap-
proach while also avoiding the use of favicon cache entries
when in incognito mode. Additionally, the Chrome team has
veried the vulnerability and is still working on redesigning
this feature, as is the case with Safari. On the other hand, the
Edge team stated that they consider this to be a non-Microsoft
issue as it stems from the underlying Chromium engine.
VIII. R
ELATED
W
ORK
Online Tracking.
Numerous studies have focused on the
threat of online tracking and the techniques that are employed
for tracking and correlating users' activities across different
websites. These can be broken down into stateful [69], [64],
[25], [86] and stateless tracking techniques [23], [10], [9], [63],
[62], [65], [73]. One of the rst studies about tracking [57]
measured the type of information that is collected by third
parties and how users can be identied. Roesner et al. [69]
analyzed the prevalence of trackers and different tracking
behaviors in the web, while Lerner et al. [52] provided a
longitudinal exploration of tracking techniques. Olejnik et
al. [64] investigated “cookie syncing”, a technique that pro-
vides third parties with a more complete view of users'
browsing history by synchronizing their cookies. Englehardt
and Narayanan [25] conducted a large scale measurement
study to quantify the use of stateful and stateless tracking
and cookie syncing. Numerous studies have also proposed
techniques for blocking trackers [39], [35], [38], [86]. On
the other hand, out paper demonstrates a novel technique
that allows websites to re-identify users. Conceptually, our
work is closer to “evercookies” – Acar et al. [9] investigated
their prevalence and the effects of cookie re-spawning in
combination with cookie syncing. The HSTS mechanism has
also been abused to create a tracking identier [30]. Klein and
Pinkas [46] recently demonstrated a novel technique that tracks
users by creating a unique set of DNS records, with similar
tracking benets to ours, which also works across browsers
on the same machine (our technique is bound to a single
browser). However, their attack is not long-term due to the
limited lifetime of caching of DNS records at stub resolvers
(between a few hours and a week) whereas favicons can be
cached for an entire year.
Browser ngerprinting.
While stateful techniques allow
websites to uniquely identify users visiting their site, they are
typically easier to sidestep by clearing the browser's state.
This has led to the emergence of stateless approaches that
leverage browser ngerprinting techniques [32], [50], [23],
[60]. A detailed survey on techniques and behaviors can be
found in [49]. Nikiforakis et al. [63] investigated various
ngerprinting techniques employed by popular trackers and
measured their adoption across the web. Acar et al. [10] pro-
posed
FPDetective
, a framework that detects ngerprinting
by identifying and analyzing specic events such as the loading
of fonts, or accessing specic browser properties. Also, Cao
et al. [17] proposed a ngerprinting technique that utilizes OS
and hardware level features to enable user tracking across
different browsers on the same machine. Recently, Vastel
et.al. [82], designed
FP-STALKER
, a system the monitors the
evolution of browser ngerprints across time, and found that
the evolution of ngerprints strongly depends on the device's
type and utilization. Other defenses also include randomization
techniques and non-deterministic ngerprints [62], [48].
Cache-based attacks.
Prior studies have extensively ex-
plored security and privacy issues that arise due to browser's
caching policies of different resources [70], [34], [80], often
with a focus on history-snifng [75], [51], [14]. Nguyen et
al. [61] conducted an extensive survey of browser caching be-
havior by building a novel cache testing tool. Bansal et al. [14]
extended history snifng attacks using web workers and cache-
timing attacks. In a similar direction, Jia et al. [40] exploited
browsers' caches to infer the geo-location information stored in
users' browsing history. While our attack similarly leverages
browsers' caching behavior, we nd that the favicon cache
exhibits two unique characteristics that increase the severity
and impact of our attack. First, this cache is not affected
by user actions that clear other caches, local storages and
browsing data, enabling the long-term tracking of users. Next,
while browsers fully isolate other local storages and caches
from the incognito mode that is not the case for the favicon
cache, allowing our attack to track users in incognito mode.
Favicons
have not received much scrutiny from the re-
search community. In one of the rst studies, Geng et al. [31]
used favicons to successfully differentiate between malicious
and benign websites. Their method had high accuracy, and
this work was the rst that evaluated and characterized favicon
usage in the wild. Chiew et al. [19] also proposed the use of
favicons for the detection of phishing pages. Finally, favicons
have been used as part of other types of attacks, such as man-
in-the-middle attacks [56], inferring whether a user is logged
into certain websites [2], distributing malware [1] or stealthily
sharing botnet command-and-control addresses [66].
IX. C
ONCLUSION
As browsers increasingly deploy more effective anti-
tracking defenses and anti-ngerprinting mechanisms gain
more traction, tracking practices will continue to evolve and
leverage alternate browser features. This necessitates a proac-
tive exploration of the privacy risks introduced by emerging
or overlooked browser features so that new tracking vectors
are identied before being used in the wild. In this paper we
highlighted such a scenario by demonstrating how favicons,
a simple yet ubiquitous web resource, can be misused as a
powerful tracking vector due to the unique and idiosyncratic
favicon-caching behavior found in all major browsers. In fact,
cached favicons enable long-term, persistent user tracking
that bypasses the isolation defenses of the incognito mode
and is not affected by existing anti-tracking defenses. Fur-
thermore, we analyzed a real-world dataset and illustrated
how immutable browser ngerprints are ideal for optimizing
low-bandwidth tracking mechanisms. When leveraging such
ngerprints our attack can reconstruct a unique 32-bit tracking
identier in 2 seconds, which is signicantly less than the
average 10-second overhead introduced by trackers on popular
websites [36]. To address the threat posed by our technique,
we disclosed our ndings to browser vendors and remediation
16

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efforts are currently underway, while we also outlined a series
of browser changes that can mitigate our attack.
A
CKNOWLEDGMENTS
We would like to thank the anonymous reviewers for their
valuable feedback. This work was supported by the National
Science Foundation (CNS-1934597). Any opinions, ndings,
conclusions, or recommendations expressed herein are those of
the authors, and do not necessarily reect those of the National
Science Foundation.
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PPENDIX
Here we provide additional details and context to clarify
our disclosure process.
Initially this project started by trying to understand how
favicons work in browsers, including how they are fetched,
their characteristics, how they are stored etc. One of our
earliest discoveries was that in the case of Firefox even basic
favicon functionality didn't seem to work correctly and thats
why we submitted a bug report to Mozilla in February 2020.
As such, we focused our exploration on other browsers where
basic favicon functionality appeared to be working correctly.
As we continued our exploration, and after other ideas didn't
pan out, we devised the tracking technique detailed in the
paper. This was a fairly straightforward technique to test and
we ran experiments to see which browsers were susceptible.
We informed all vulnerable browsers through reports in June
2020, detailing our technique and the underlying issue – as
we wrote in the paper “we have disclosed our ndings to all
affected browsers”. This disclosure took place even before we
submitted our paper to NDSS in July 2020. We opted for a pre-
submission disclosure to give vendors ample time to address
the issues we had uncovered. We made our ndings public
many months later (February 2021) after the camera ready
version of our paper was accepted by NDSS. Not notifying
Firefox at that time was denitely an oversight, which was a
result of us focusing on which browsers were vulnerable and
notifying those. This wasn't done to harm users in any way,
nor was it done to improve the paper's impact as claimed by
certain people online. This is further corroborated by two facts:

We already explicitly mentioned in the paper that our
attack didn't work against at least one browser (Tor).

We didn't notify Firefox even after our paper was ac-
cepted, at which point whether Firefox was vulnerable
or not would obviously not improve the paper or change
anything for us. Again, this shows that we simply (and
unwisely) didn't take Firefox into account during the
disclosure process because it wasn't susceptible to the
attack. It wasn't due to some elaborate or malicious plan.
We agree that we should have proactively notied Firefox
to make sure that the attack wouldn't work no matter what
changes they eventually made to their favicon functionality.
In hindsight, this seems like a painfully obvious thing to
do, but this past year has been far from normal. We hope
that the conversations we had with the vulnerable vendors
to get this security bug xed shows that we have the best
interests of web users at heart. This can also be seen by our
track record of responsibly disclosing our research, including
two other disclosures that Firefox was a part of in the past
year alone. Without an understanding of the larger context
around our work, its denitely understandable that people
would get the impression that we wanted Mozilla to introduce
a weakness in their browser, but we trust that members of
our research community will be able to better evaluate the
situation. Neglecting to circle back with Mozilla about the
security issue that could potentially be introduced in their
browser was an oversight, and thankfully the overarching
favicon caching aw is now well known and any x to Firefox
will include appropriate proactive mitigations further ensuring
that no users will be affected by this vulnerability.
19