Understanding PAN Enumeration Attacks: A Step-by-Step Breakdown.

In the realm of digital security, credit card fraud has taken on new, more sophisticated forms, such as PAN (Primary Account Number) Enumeration attacks.

Unlike traditional methods that rely on stolen card details, these attacks involve the automated generation and validation of credit card numbers.

This article offers an in-depth exploration of the structure and mechanics of a PAN Enumeration attack, its implications, and preventive measures.

What is a PAN?

• Definition A Primary Account Number (PAN) is the number embossed or printed on a debit or credit card. It uniquely identifies the card issuer and the cardholder account.
• Card Number Components Typically 16 digits (sometimes 19) in length, a PAN includes the Issuer Identification Number (IIN), the individual account identifier, and a check digit.

Types of Cards Affected

• Credit Cards All major credit cards, including Visa, MasterCard, Discover, American Express and many retail close-loop gift cards, use PANs and are susceptible to enumeration attacks.
• Debit Cards Similarly, debit cards associated with major networks are also at risk.
• Other Payment Cards Prepaid cards, gift cards, and other card-based payment systems that follow similar numbering schemes may also be vulnerable.

Unraveling the Myth of Randomness in Payment Card Numbers

In the design of a 16-digit credit card number, there is a surprisingly limited scope for randomness, with only about 6 to 9 digits actually varying.

The first six digits, known as the Issuer Identification Number (IIN) or Bank Identification Number (BIN), are static.

They serve a critical function in identifying the card's issuing association and delineating its range.

Following the IIN/BIN, the next one to two digits are typically allocated for sub-binning, which is used to categorize the card into specific programs or business lines.

The final digit of the card number is reserved for the Luhn Check digit, a security feature designed to validate the number's authenticity.

Card technology predates the commercial uses of the computer and the internet. The concept of credit cards dates back to the 1950s, a time of burgeoning financial innovation.

However, their widespread adoption coincided with the commercialization of the internet. In a way, the internet era embraced one of the most insecure payment methods as its transactional standard.

By their very structure and the monolithic nature of their underlying infrastructure, the more cards that are issued under an IIN/BIN, the more inherently insecure the entire range actually becomes. Isn’t that ironic?

This choice of adopting credit card systems, which inherently had limited randomness and security in their number structure, can retrospectively be seen as a significant oversight, especially considering the evolving challenges in digital security.

Dissecting a PAN Enumeration Attack

Step 1: Decoding Credit Card Number Structures

• Understanding the Luhn Check Digit Algorithm Attackers begin by learning that credit card numbers adhere to the Luhn algorithm, a formula for validating card numbers.
• Issuer Identification Number (IIN) The publicly available IINs, the initial digits of a card number, identify the card issuer and are crucial for generating plausible numbers. You can actually make all the card numbers within a IIN/BIN in a spreadsheet, minus the check digit. That’s already knowing 15:16 numbers for an entire issuing processor’s BIN (with hardly any technical ability).

Step 2: Generating Potential Card Numbers

• Creation of Numbers Attackers employ software programs to create every possible card number within the IIN/BIN range. This process involves the integration of both the Issuer Identification Number (IIN) and the Luhn algorithm.

Step 3: Employing Automation Techniques

• Bot Testing Bots test the generated numbers across various online platforms, significantly speeding up the process.

Step 4: Testing Generated Numbers

• Small Transactions Cybercriminals often perform minor transactions or authorizations on websites with less robust security measures. This tactic is used to confirm valid credit card numbers, expiration dates, and CVV codes through a trial-and-error method.
• Response Analysis The transaction responses are crucial for determining valid numbers.

Step 5: Validating and Utilizing Numbers

• Exploitation Once a number is validated, it can be used for fraudulent transactions or sold on the dark web.

Challenges in Detection and Advanced Techniques for Processors

• Volume and Speed The high volume and speed of attempts complicate detection.
• IP Address Masking Attackers often use VPNs or proxies.
• Mimicking Human Interaction Some bots can mimic human behavior to bypass detection.

Implications for Consumers and Businesses

• Financial Loss Consumers risk unauthorized transactions.
• Security Costs Businesses face increased costs in fraud prevention and potential reputational damage.

A Laundry-list of Mitigation Strategies

• Investing in Sophisticated Fraud Detection Systems Prioritizing the adoption of advanced technology to detect and prevent fraudulent activities effectively.
• Ongoing Security Evaluations Regularly conducting thorough security audits to uncover and resolve potential vulnerabilities.
• Vigilant Chargeback Monitoring Actively tracking unauthorized transactions, with a keen eye for patterns and signs that could indicate enumeration attacks.
• Implementing Card On/Off Switches and Limits Encouraging the use of cards only when necessary, with the ability to 'turn off' the card to prevent unauthorized use.
• Merchant-Lock Schemes on Virtual Cards Setting merchant-specific limits on virtual cards to ensure they remain secure, especially for recurring subscription services.
• Rethinking Physical Card Usage Advocating for physical cards without printed numbers in most scenarios, emphasizing their use solely for transactions at trusted physical POS terminals.
• Virtual Card Strategies for Online Transactions Promoting the use of virtual cards for online purchases and setting up subscription services, empowering consumers to take control over their subscriptions and enhance security.
• In-App Provisioning to Wallets (Card Detail Insert Validation) When adding a card to a digital wallet, it's advisable that wallet providers prevent the same card from being registered across multiple users' wallets to minimize unauthorized use. However, in-app provisioning isn't a foolproof solution and requires careful monitoring. Digital wallets should also consider implementing sophisticated methods to safely accommodate scenarios where sharing cards among different users is necessary. Yet, this sharing feature should be limited due to the inherent security risks associated with card-based networks, especially due to the static nature of Bank Identification Numbers (BINs) or Issuer Identification Numbers (IINs), which lack randomization capabilities.

These steps are aimed at revolutionizing the way consumers and businesses approach card security, shifting the power dynamics in transactions and subscription services while ensuring utmost security and keeping the “House of Cards” from toppling due to too few random numbers. You can’t simply tokenize an inherently insecure thing to make it more secure, it’s impossible to do that. The secret is shared already or it’s just way too easy to derive.

Conclusion

In the digital transaction realm, PAN Enumeration attacks present a significant challenge. It's vital to understand their methods to create effective defensive strategies, necessitating awareness from both consumers and businesses. With the evolution of technology, our tactics to combat these advanced forms of fraud must evolve as well.

Owning a newer Bank Identification Number (BIN) and controlling the entire BIN range can offer temporary protection against such attacks. However, the more cards issued under a BIN, the more vulnerable they become to this type of fraud. It is crucial to begin pattern monitoring from the outset, as this threat is likely to affect every participant in the card payment industry eventually, It feels like I’ve combatted them (in the trenches) for decades. By there structure and the underlying infrastructure, the more cards that are issued under an IIN/BIN the more inherently insecure the entire range actually becomes, isn’t that ironic?

A secure card strategy involves deactivating (or more accurately, gating) the card until needed or limiting access through specific merchant permissions for subscription-based pull transactions. This approach combines the security of a push transaction feel (although it's actually a pull-transaction) with the ability to set rules for authorized pulls, so all “unauthorized” decline (unless later permissioned). Enumeration attacks are primarily responsible for the chargebacks (that entire industries have been built around), creating a persistent "whack-a-mole" problem in fraud management.

Additional Resources:

https://en.wikipedia.org/wiki/Luhn_algorithm

https://usa.visa.com/content/dam/VCOM/global/support-legal/documents/visa-guidance-to-guard-against-enumeration-attacks.pdf

https://usa.visa.com/content/dam/VCOM/global/support-legal/documents/anti-enumeration-and-account-testing-best-practices-merchant.pdf

The Original Article was Published here: https://www.paytech.services/blog/understanding-pan-enumeration-attacks-a-step-by-step-breakdown