A BIN number, or Bank Identification Number, is the initial sequence of six to eight numbers that appears on a credit card, debit card, or other payment card. This sequence identifies the card issuer and facilitates electronic financial transactions by making sure the charge is sent to the correct bank for payment.

Beyond identifying the issuing bank, the BIN can also provide information about the card type (e.g., credit, debit, or gift card), the card level (e.g., standard, gold, or platinum), or the geographical location of the issuing institution. BIN numbers play a key role in security and fraud prevention for electronic transactions, verifying the authenticity of the card being used and allowing payment processors to apply appropriate risk management processes.

BIN attacks are a form of credit card fraud where cybercriminals use brute-force methods to guess valid combinations of credit card information. In 2022 alone, credit card fraud losses amounted to $219 million in the US. During a BIN attack, criminals systematically test many combinations of credit card numbers, expiration dates, and card verification values (CVVs). Once they find a working combination, they test the card by making small purchases. These attacks are challenging for financial institutions, as they lead to financial losses, reputational damage, and operational disruptions.

In this article, we’ll discuss what businesses need to know about BIN attacks, including how they work and the steps to prevent them.

What’s in this article?

• How do BIN attacks work?
  
• Which businesses are most vulnerable to BIN attacks?
  
• How to prevent and mitigate BIN attacks
  
• Best practices for protecting your business against BIN attacks
  

How do BIN attacks work?

BIN attacks involve fraudulent actors exploiting the BIN to generate valid credit or debit card numbers for illicit purposes. The BIN is the initial sequence of six to eight numbers on a credit or debit card that identifies the issuing bank and card type. Here’s a detailed breakdown of how these attacks occur:

• BIN identification: Attackers first acquire the BINs of specific banks or card issuers. This can be done through various means, such as purchasing BIN lists on the dark web, extracting them from stolen card data, or using publicly available information.

• Card number generation: Attackers generate complete card numbers by appending randomly generated digits to the BIN and calculating the appropriate check digit (usually the last digit) using the Luhn algorithm, which is a formula used to validate a variety of identification numbers. More sophisticated BIN attackers might use automated scripts or bots to generate and test thousands of card numbers, increasing the efficiency and scale of the attacks.

• Validation attempts: The generated card numbers are then tested on websites, usually those with weak security measures or where the verification process doesn’t immediately involve an actual transaction (such as adding a card to a digital wallet or checking the card’s validity on certain online platforms).

• Exploitation: Once a valid card number is identified, fraudulent actors can use it to make unauthorized purchases or transactions until the card is blocked or the fraud is detected by the cardholder or the issuing bank. Fraudulent actors may use card details to make online purchases, create counterfeit cards, or sell the information on the dark web.

These attacks can create financial loss for customers and businesses, damage trust in financial institutions, and increase operational costs related to fraud detection and prevention.

Which businesses are most vulnerable to BIN attacks?

Businesses most susceptible to BIN attacks are typically those that process a high volume of online transactions and may not have stringent verification processes in place.

• Online retailers: Ecommerce businesses that accept online payments are often targeted by BIN attacks due to their higher volume of transactions and the potential for anonymity in online purchases.

• Digital goods sellers: Businesses that sell digital goods such as software, music, or videos are at risk because digital products can be quickly and easily obtained—making them an attractive target for fraudulent actors.

• Travel and hospitality businesses: Airlines, hotels, and travel agencies are susceptible to BIN attacks as they often deal with high-value transactions and international clients—making it harder to detect fraudulent activities.

• Gaming and gambling companies: Online gaming and gambling platforms are frequent targets for BIN attacks due to the instant and high-value nature of transactions, as well as the potential for anonymity.

• Subscription-based services: Companies offering subscription-based services such as streaming platforms or membership websites are at risk because fraudulent actors can exploit recurring billing models to carry out fraudulent transactions over an extended period.

• Financial institutions: Banks and financial institutions themselves can be targeted by BIN attacks, as fraudulent actors seek to exploit weaknesses in the payment processing systems and conduct unauthorized transactions.

• Payment processors: Companies that handle payment processing for other businesses are also vulnerable to BIN attacks, as a breach in their systems can have detrimental consequences for businesses and their customers.

How to prevent and mitigate BIN attacks

• Behavioral analytics and machine learning: Implement advanced machine learning models and behavioral analytics to detect anomalous transaction patterns. These systems can learn from historical transaction data, recognizing patterns that indicate fraudulent activity and adapting to new threats over time.

• Tokenization and encryption: Beyond basic encryption, use tokenization to replace sensitive card details with a unique identifier (token) that has no value if breached. Ensure that tokenization is applied across all systems where card data is processed or stored. Consider also employing advanced cryptographic methods such as homomorphic encryption, which allows for computations on encrypted data, providing an additional layer of security.

• 3D Secure 2: Implement the latest version of 3D Secure, which adds an additional layer of authentication for online transactions and supports a more simplified user experience while providing stronger fraud prevention capabilities.

• Network-level fraud detection: Use advanced network-level analytics to detect and prevent BIN attacks. This can include analyzing network traffic for suspicious patterns, monitoring for signs of data exfiltration, and implementing an advanced intrusion detection system (IDS).

• Endpoint security: Ensure that all endpoints, especially those involved in transaction processing, are secured with advanced endpoint protection platforms (EPPs) that include next-gen antivirus, endpoint detection and response (EDR), and zero trust security models.

• Advanced application security: Employ comprehensive application security measures, including regular code audits, application-level encryption, and web application firewalls (WAFs) that are configured to detect and block attack patterns specific to BIN attacks.

• Deep packet inspection (DPI): Use DPI at the network perimeter to scrutinize incoming and outgoing traffic at the application layer. This can help identify and block potentially malicious packets that could be part of a BIN attack.

• Rigorous authentication: Incorporate biometric verification methods such as fingerprint or facial recognition for additional layers of authentication, particularly for changes to payment methods. For high-risk transactions, consider implementing consent-based authentication, where the user must explicitly approve the transaction through a separate, secure channel.

• AI-powered risk scoring: Develop or integrate AI-based systems that assign risk scores to transactions in real time based on a multitude of factors, including user behavior, device fingerprinting, and transaction context. Transactions with high-risk scores can trigger additional verification or be blocked outright.

• Cross-channel and geolocation analysis: Conduct analysis across different channels (e.g., online, mobile, and in store) to detect patterns and linkages in fraudulent activities. This holistic view can uncover sophisticated fraud schemes that exploit multiple channels. Also analyze the geolocation data of transactions to detect discrepancies, such as a card being used in two distant locations within an implausible time frame.

• Blockchain for transaction security: Explore the use of blockchain technology for its immutability and transparency features to secure transactions. Smart contracts can be employed to automatically enforce transaction rules, reducing the potential for fraud.

Best practices for protecting your business against BIN attacks

• Segment your network: Divide your network into segments to limit the spread of potential intrusions and facilitate more focused monitoring. Monitor segments that handle sensitive payment information to detect and respond to unusual activities promptly.

• Stay updated on threat intelligence: Use real-time threat intelligence feeds to stay updated on the latest BIN attack tactics and indicators of compromise (IOCs). This information can help you update defensive measures and more effectively respond to new threats.

• Plan a proactive incident response: Have a sophisticated incident response plan specifically designed for handling BIN attacks. This should include procedures for fast detection, containment, eradication, and recovery, along with communication strategies for stakeholders.

• Continuously reassess security protocols: Regularly assess your security posture through penetration testing, vulnerability assessments, and security audits to identify and address potential weaknesses that could be exploited in a BIN attack.