Tip of the iceberg:
6M stolen cards
analyzedIntroduction
Digital systems likepayment cards comewith inherent security
and privacy risks, but
our everyday lives arebuilt around the fictionthat they are completely
trustworthy. What are
the real risks of having
your card details stolen?
What other information isup for grabs by hackers?
How can we mitigate
those risks?

Card theft is a global problem. The 2020 Nilson Report showed that it led to $28.65 billion in losses
around the world, with over one third of the losses centered in the US. Statista found that almost
7% of card sales in the US were fraudulent, predicting that total global losses will reach around$32.96 billion by 2023.
Technology plays a massive role in card fraud and theft. As we showed in NordVPN’s research onbrute force attacks, computers can be used to guess card details. Payment information may beleaked as a result of data breaches and system hacks. Criminals do not even need to do the dirty
work themselves — the dark web hosts thriving marketplaces for stolen card details.
In this report, we present our research into payment card sales on the dark web. Our findings
are based on a dataset of almost 6 million cards across eight major markets. We examine thebackground of how the cards and other personal data were stolen, detailing the methods ofour study. We analyze the distribution of stolen cards by country, along with the types of cards
available and their prices. We also expand on existing research by examining the connection
between stolen card details and other personal information, trying to determine how many card
details are hacked, leaked, or stolen instead of using brute-force methods.
Finally, we compare these results with the available population and card penetration data to forma risk index for different countries. We use our findings to provide recommendations for users andbusinesses on how to prevent the theft of card details and other personal information.

Key takeaways

US cards are the biggest
target — over half of all
payment cards discoveredcame from the US.

The dark web marketplaces
had the data of 6 million
stolen payment cards at thetime of our research, with 2.5
million being up for sale formore than $18.5 million in
total. If purchased, thesepayment card details could
net criminals much more thanthey originally paid for them.

Over 60% of cards were sold alongsideother personal information relating to
the victim, such as their address,
phone number, email address, date ofbirth, or Social Security number.

Key takeaways

Russians were theleast likely to find theirdata on the dark web
marketplaces
surveyed.

Cards sell for an
average of $7,
but many areleaked for free.

2

Background
Card fraud often leaves the victim helpless — inlarge part because it is near impossible to tell
how your card details have been stolen. Even
security professionals are at risk. But such brute
force attacks (guessing the details, confirming
them on websites with fewer security checks,
then exploiting or selling them on the dark web
as detailed in our previous research) are just one
way to get payment card details. More direct
methods of hacking and theft can make victims
feel even more vulnerable — and grab more thanjust their card details.
Identity theft has been described as “thequintessential crime of the information age.” If adata breach or hack exposes not just your card
details, but also your address and other personal
information, it can lead to more cases of identity
theft. Anyone can become a victim of identity
theft, and it can take years to undo the damage.
Once the attacker has obtained the victim’s name,home address, and email address, they may evenabuse legal methods (such as using the GDPR’sright to access for more personal information) infurthering the identity theft scheme or committing
other malicious activities.

Furthermore, while credit card fraud tends
to affect wealthier individuals (leading to its
portrayal as a “victimless crime” in popular culture
because the victims are privileged and credit
card companies often refund fraudulent charges),other forms of identity theft like bank accounttheft often affect the underprivileged.
In this study, we focus on cases where thecard details are sold on the dark web alongsideother personal information. This ups the stakes,
providing additional value (using the information
for identity fraud), but it also causes additionalharm to the victim (more information exposedand greater risk of identity theft). Such bundles
of information come from cybersecurity breaches(hacks, leaks, theft) that could be avoided using
appropriate protective measures. But to protect
ourselves, we must first understand how card
details and other information may be stolen.

Credit card and data theft
We’ve divided the different methods of theft into a loose
categories by the type of attack used:• Traditional methods
PhysicalSocial• Hacking methods
Devices
Systems
We have further divided these categories
by scale:• Small scale: Tends to operate on an individual basis.
• Medium scale: Less targeted, may be long-term with thepotential to scale up.
• Large scale: Can be easily automated or affects a huge
number of people at once.
Of course, these methods of theft can and often dooverlap. One method may even lead to another — forexample, a data breach leaking email addresses can leadto a large-scale phishing attempt. Phishing itself covers
both social attempts and automated digital methods.

We will examine each of these methods for stealing card details.

Card loss or theft is perhaps the simplest method used, involving
the physical loss of the card itself. Victims tend to be alerted to thismethod sooner than the others in this list.
Dumpster diving has been described as “no tech hacking.” It may
involve searching for personal data, bank statements, or old credit
cards in domestic trash, or raiding corporate waste disposal facilities
for sensitive documents holding larger sets of customer data. It may
involve social engineering, such as when it leads to identity theft orwhen behavioral methods are used to gain unauthorized access toconfidential waste.
Shoulder surfing or copying is another form of no tech hacking. It
involves looking over someone’s shoulder on the train or in a busy
café to observe their password, card number, or other information.
It also includes sales employees copying card details as they takepayments. This method of data theft is very low tech, but it ispopular and effective for small-scale criminals.
Family or “friendly” fraud often occurs when those close to us
abuse access to our personal information and physical cards, but“friendly” fraud may also be perpetrated by dishonest consumers— for example, by making purchases that are later charged backvia the consumer’s credit card. This practice ends up costing theseller money, which can be particularly damaging for small online
businesses.
Card skimming involves criminals adding an extra device to an
ATM to copy the victim’s card details. Despite having been aroundfor decades, card skimming devices are easy to miss if you’rebusy or aren’t actively looking for them. Different types of cardskimmers have varying levels of concealment, with some being quite
sophisticated in altering the ATM.

Scam phone calls are one of the earlier examples of social
engineering. In such calls, the scammer pretends to be your bankor other trusted organization to trick you into revealing sensitive
information. Governments often publish advice and list ways you
can report scam phone calls.
Phishing is a widely known method of attack, mixing more
traditional cons with digital techniques (such as spam emails or
fake sites) to trick victims into divulging their login details, accountinformation, or card number. Phishing manipulates the cognitive
vulnerability triggers that play into our expectations. There are steps
you can take if you become a victim, but it is best to act quickly.
RFID involves hacking a payment made with a physical card in close
proximity. A contactless payment may be copied and relayed tomake payments in other countries. Banks have taken steps to curb
RFID attacks.
Public Wi-Fi poses serious security issues, especially when working
alongside unknown and possibly malicious devices. The public
seems to be aware of at least some of these risks, with users
resorting to different behavior patterns for protection. For example,
many people tend to avoid online banking when on public Wi-Fi— but they still go on other sites (like social media platforms) that
could leak important information and result in identity or credit
fraud.
The hacking of online bank accounts tends to affect those inalready marginalized groups. The increasing links between banks
and emerging fintech firms create additional cybersecurity risks.
Institutions may promote protective measures in the form ofperceived risk, security-promoting interfaces, institutional trust, andreassurances (like money-back guarantees), although they do notnecessarily mitigate against the longer-term potential impact andeffects of identity theft.

POS malware involves infecting point of sale (POS) devices inshops with viruses that send card details to hackers. These
attacks range from targeting specific shops to large-scale hacks
when enough POS devices in a large store are compromised. For
example, in 2013 the retail chain Target fell victim to this kind of
attack. The threat of malware is especially prominent now that
stores are moving towards tablet- and phone-based POS, withcriminals continuing to improve their methods to avoid detection.
Malware on devices is a common problem with a large“underground economy” of malware as a service. Malware
present on your device can scrape data directly, snoop on
other apps (acting like a “ghost in the browser”), or otherwise
compromise the security of your personal or company device tosend data back to the criminals.
Brute force was explored in detail in an earlier study by NordVPN.
A brute force attack involves trying various combinations toguess the card’s details. These attacks are made easier by thefact that cards from particular countries, card issuers, and banks
follow certain patterns. The guesses can be rapidly checked en
masse on sites with lax payment security.
Formjacking is a recently discovered form of web-based attack
that uses malicious JavaScript and other techniques to takeover online forms and leak the data to hackers. Formjacking
is difficult to detect and capable of exploiting a wide range ofother vulnerabilities in websites and browsers. It becomes morecommon in the run-up to the winter holiday period as online
shopping increases.

Data breaches are a major reputational risk for companies anda real risk for their customers. The 2015 PwC report to the UK
government found that not only had the number of incidents
increased compared to the year prior but also that their scaleand cost had doubled as well — to the extent that for large
businesses they should be considered a “near certainty.” Whilesome high-profile breaches do make the news, the situation isoften misrepresented and misunderstood. Users often end up
mistakenly blaming their own habits rather than the breachedorganization or hackers, and often underestimate the potential
effects to them personally even when they know of the generalrisks. User-friendly tools are essential when it comes topublic awareness and action following a breach. For example,
password breach notifications can prevent credential stuffing
attacks, limiting the effects of identity theft that results fromleaks of personal information.

The table above tries to put each attack type discussed into its
appropriate category. This study will focus on the highlighted
types because they involve more direct digital hacking
techniques and may (to an extent) be mitigated using proper
behavior and tools.
It is important to stress that all the attack types above can leadto payment card theft as well as wider cases of identity fraud.
When more information is leaked alongside card details, it
becomes easier for hackers (and those they sell the data to) tocause more long-term harm to their victims.

Methods
To study the extent of
online card theft, we
worked with a set ofdetails from millions ofcards for sale on the darkweb. In particular, we
examined the cards thatcame bundled with otherpersonal information todetermine how many were
hacked or stolen rather
than simply brute forced.

Data collection
The data was compiled in partnership with independent researchers specializing incybersecurity incident research. They evaluated eight key marketplaces on the dark web
to retrieve the details of over 6 million cards, including details of the type of card (credit ordebit) and other linked personal information. The data NordVPN received from these thirdparty researchers did not contain any information that relates to an identified or identifiable
individual (such as names, contact information, or other personal information). The study
did not determine the exact number or analyze the entirety of payment card details sold on
the whole of the dark web — NordVPN only examined the set of statistical data provided byindependent researchers.

Data cleaning
Given the nature of dark web marketplaces, not all dark web sellers could be fitted into
standard data categories or labels. To create a consistent dataset, we excluded entries
with incomplete or incompatible data. These excluded records represent less than 1.5% of
the total set — around 3,500 were excluded due to errors in loading the data and a further
80,000 due to unsuitable labeling. This left us with a dataset of 5,953,651 cards.
We arranged the data by country where the card was issued. This initially resulted in over
200 categories, which we narrowed down by focusing on the countries that had more than
1,000 cards in their dataset. This was due to a large number of countries having only ahandful of cards that skewed the data when making international comparisons. This left us
with 98 countries for comparison, a list generally mirroring the countries’ populations. The
data from the remaining countries was folded into the category of “no/other country” to keepthem present in the set.

Data extraction
From our dataset, we extracted the following
information, separated by country:• Count• Average price• CardType: credit, debit, other
Brand: Visa, Mastercard, Amex, other• Additional information
Home address
SSN
DoB
Phone number
Email address
We then calculated the percentage of the cards
that were obtained by way of hacking, theft, or
data breaches. This was based on the number
containing one or more other pieces of information(home address, SSN, DoB, phone number, or email
address). The remainder may have been brute
forced because they only contain the card data
itself. However, the percentage of hacked cards is
a conservative estimate — it is likely that at least
some card details categorized as “brute forced”
were also a result of theft or breach. Meanwhile,
the percentage of brute forced cards represents
the maximum possible number of brute force
instances (it is highly impractical to brute force

other pieces of information in addition to thecard details).
We compared the statistical card data between
countries with UN population stats and thenumber of cards in circulation by country or
region from Visa, Mastercard, and American
Express. Using the information below, we createda risk index to compare the chances of citizens
from different countries finding their card dataon the dark web:
Number of cards in the dataset (N)Population (P)Number of cards in circulation per person (C)The percentage of cards that were definitely
stolen by hacking (H)The final variable was added to account for
the additional risk of certain countries being
greater targets for hackers. Brute force attacks
can potentially affect all countries, while major
breaches and hacks tend to focus on specificbusinesses, locations, or other entities linked to
specific countries. The risk index was calculatedusing the following formula:
RI =

*H

With these data types, we were able to analyze
country patterns and differences.

Results
Our study shows that large numbers of card details
are being stolen alongside other sensitive personaldata, posing a significant risk of identity fraud. The
details of millions of cards are being leaked for free
on dark web markets. The average price of the 2.5
million cards available for sale was $7.01, with thewhole studied dataset of stolen details being worth$18.5 million.
The table at the end of this section shows the top
20 countries by the following key metrics: numberof cards, average price, and percentage of cards
with additional information (minimum requirement
for hacked cards) as well as the highest and lowest
risk indexes.

Cards
Out of our dataset containing almost 6 million
cards, over half were from the US (3,461,444 cards,
or 58.1%). This makes sense, considering the US
has a higher rate of card penetration, a sizable
population, and a strong economy. Next were India(3.7%), the UK (2.8%), and Mexico (2.6%),
followed by Brazil, China, France, and Italy — amix of countries with high populations and strong
economies or political reputations.
Out of the 2.5 million cards that were sold on
dark web marketplaces, cards from Denmark
commanded the highest average price at $11.54
per card. This was followed by cards from Japan,
Portugal, and Ukraine, all over $11 on average.
Interestingly, cards from the US were comparatively
cheaper — at $6.86, they fell just below the overall
average cost of $7.01. Cards from Argentina and
New Zealand were the cheapest, averaging lessthan $2.50. Considering the amount of damage
that can be caused with stolen card data and any
bundled personal information, this is a shockingly
small amount, suggesting that the use of stolencards could itself be relatively scalable even by
those who have obtained the details second-handfrom the dark web.

Additional information
Over half (51.5%) of the cards came withaddresses, while a significant number came withphone numbers (39.8%) and email addresses(28.7%). Few cards came with a date of birth(2.5%) or a Social Security number (1.8%) —however, this additional information would expose
the victim to a significantly higher risk of identity
fraud.
Overall, 62.8% (almost two thirds) of the cards
came with some form of additional information,
indicating hacking, while up to 37.2% were
guessed through brute force attacks (remember,this is an upper bound for brute force attacks anda lower bound for hacks). This means that the
majority of stolen card details were obtained by
one of the hacking methods discussed earlier.
The numbers vary by country. Of particular noteis that records without valid country information
followed an inverse trend, with almost all entries
only containing card details (92.4%). In general,countries with more cards in the dataset had
higher rates of cards with additional information,with the exception of China (which possibly
bucked the trend due to a lack of integration with
global information systems and the closed-off
nature of the Chinese internet, online shopping
platforms, and banking platforms).

Almost 90% of cards from India came withadditional data, exposing Indian victims to wider
issues of identity fraud. Generally, European
countries and more economically developed
countries had higher rates of additional
information, showing a loose correlation between
the perceived value of the victim and hacking
attempts.

Risk index
Using the calculation outlined above, we createda risk index for 98 countries. Our index takes into
account the likelihood of one of your cards being
on the dark web as well as the extra risks posedby having it sold with additional information.
The countries with the highest risk index (andtherefore the greatest risk) were Malta, Australia,
and New Zealand. This is potentially due to theirassociation with the EU or the Anglosphere while
having smaller populations compared to theirfinancial and political weight. The US was ranked
5th, while Singapore and Hong Kong were 9th and10th respectively.
On the other end of the spectrum, Russia had thelowest risk score and China was 3rd from last.
This matches with the prevailing hypotheses
regarding the location of large-scale hacking
operations and the purposeful targeting of AngloEuropean countries. Other countries lower on therisk index tended to have smaller economies (withthe exception of Germany, which was 15th fromlast).
The risk index does not necessarily correlate to
the number of cards in the dataset, showing theimportance of taking into account population size,card penetration, and other factors to assess risk.
There is also the case of Germany, which despitea strong economy and a sizable population for aEuropean country, had a very low risk rating. Thismay be due to political and behavioral factors,
such as a greater focus on regulations as wellas increased security and privacy measures
implemented for individuals and organizations.

Top 20 countries by different categories of data
Country

Cards%

Country

Avg price

Country% HackedCountry

Risk index

Country

RI (lowest)

USA

346144458.1

Denmark$11.54

India

89.8

Malta

1

Russia0

India

2180533.7

Japan$11.07

Slovakia

86.3

Australia

0.91

Pakistan

0.01

UK164143

2.8

Portugal$11.07

Saudi Arabia

82.8

New Zealand

0.84

China

0.04

Mexico156613

2.6

Ukraine$11.02

Portugal82

Slovenia

0.82

Bangladesh

0.06

Brazil144297

2.4

Slovenia$10.83

Ukraine

82

USA

0.79

Venezuela

0.09

China

114765

1.9

Vietnam$10.74

Chile

81.4

Bahrain

0.79

Ukraine

0.16

Canada97552

1.6

Slovakia$10.64

Hungary

79.2

Bahamas

0.78

Indonesia

0.16

France

97032

1.6

Chile

$10.55

Japan

78.9

UAE

0.78

Egypt

0.18

Italy

78676

1.3

Saudi Arabia$10.55

Slovenia

77.2

Singapore

0.77

Benin

0.18

Australia

65350

1.1

Thailand$10.27

USA

76.8

Hong Kong

0.77

Zimbabwe

0.2

Spain

60173

1

Singapore$10.20

Iceland76.3

Namibia

0.77

Paraguay

0.21

South Africa

46737

0.8

Philippines$10.00France74.9

Quatar

0.74

Iraq

0.26

Türkiye

41803

0.7

Mauritania$9.98

Bahrain

74.6

Botswana

0.73

Vietnam

0.26

Chile

30079

0.5

Hungary$9.71

Denmark

74.1

Chile

0.72

Nigeria

0.27

Germany

26558

0.4

France

$9.42

Italy

73.3

Puerto Rico

0.72

Germany

0.28

Malaysia

22504

0.4

Uganda$9.04

Russia73.2

Eswatini

0.72

Morocco0.3

Argentina

16792

0.3

Cameroon

$8.98

Iraq

72.9

Iceland0.71

Colombia

0.31

Peru

13968

0.2

Italy

$8.90

Greece72.2

Türkiye

0.71

Poland0.31

Denmark

12468

0.2

Russia

$8.62

Switzerland

72.1

Oman

0.71

Mozambique

0.31

Norway12055

0.2

Brazil

$8.47

UK

71.9

Hungary

0.68

Tunisia0.32

Recommendations
Banking companies can reduce risks throughtechnological means, but this leaves them to deal
with invasive and costly monitoring of customers
and potentially fraudulent transactions. Similarly,banks and online shops simply cannot control thewider issues associated with identity theft anddata breaches. It is often up to the user to protect
themselves against these risks by incorporating
online privacy and security tools into their daily life.
We recommend reading our guide on how toprotect yourself against identity theft or trying
these specific steps and using NordVPN tools
against specific threats:

VPN for online banking• Protect your network traffic, especially on
public Wi-Fi, to keep your personal andfinancial information secure.

Threat Protection• Scan for malware during downloads
to remove potential threats from both
personal and business POS devices.
• Block malicious websites that might leakyour data or malicious ads that could
attempt formjacking.
• Combined with VPNs and othercybersecurity tools, active threatprotection can help protect against
phishing attempts by hackers to get
victims to reveal private information.

NordPass• Use unique, complex passwords toprotect your online banking and otheraccounts against brute force attacks andother hacking methods.

NordLayer• Reduce the risk of breaches for yourbusiness and protect your (and yourcustomers’) data with multi-layered andscalable network protection — especially
if you have remote workers.

Dark web monitor• Get alerts if your credentials appear on
the dark web following a brute force
attack or a data breach, giving you aheads up and preventing criminals fromgetting a head start.

Because there are different ways for stealing,hacking, or leaking card details or personaldata, it’s best to take a multi-pronged approachto security. Combine these tools to reduce theoverall risk of your details being sold on thedark web — and the accompanying long-term
consequences of credit and identity theft.

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2023