What are BIN attacks and how do they work? (Card number-based attacks, protection methods)

[Student]

BIN attacks (Bank Identification Number attacks) are a type of cyber fraud used in the context of carding, where attackers exploit the structure of payment card numbers to create or verify potentially valid number combinations. These attacks are aimed at financial gain, such as conducting unauthorized transactions, purchasing goods or services, or reselling stolen data on the black market. For educational purposes, I will explain in detail what BIN attacks are, how they work in the context of carding, and the available defense methods, while avoiding providing instructions that could facilitate illegal activity.

What is a BIN and its role in carding?​

BIN (Bank Identification Number) is the first 6-8 digits of a payment card number (credit, debit or prepaid) that identifies:

• Issuing bank (that issued the card).
• Card type (e.g. Visa, Mastercard, American Express).
• Card category (classic, premium, corporate, etc.).
• Country of origin of the issuer.

For example, BIN 414720 identifies a Visa card issued by a specific bank. This data is publicly available in databases such as binlist.net or can be collected from data breaches.

In the context of carding, BIN attacks are used to generate or verify card numbers, which are then used for fraudulent transactions. Carding is the process of using stolen or generated card data for purchases, cash withdrawals, or to test the validity of cards on vulnerable platforms.

How BIN attacks work​

BIN attacks are an automated process that involves several stages. Here's a detailed description of their mechanism:

1. Collecting BIN information:
   • Attackers obtain BINs from open sources, databases purchased on the darknet, or through data leaks.
   • They may also use social engineering, phishing, or data interception to obtain the BIN.
2. Card number generation:
   • Using a known BIN, scammers generate the full card number (usually 16 digits for Visa/Mastercard). The remaining digits (except for the checksum) are randomly entered.
   • To ensure the validity of the number, the Luhn Algorithm is used, which generates the last digit (checksum) so that the number complies with payment system standards.
   • Example: For BIN 414720 xxxx xxxx xxxxx the Luhn algorithm adds the last digit to make the resulting number look valid (e.g. 4147201234567890).
3. Testing numbers:
   • Generated numbers are verified on online platforms such as online stores, subscription services, or payment gateways where minimal verification is required.
   • Fraudsters use a "BIN check" technique, entering a card number into a payment form to check whether the card is active without completing the full transaction.
   • Vulnerable platforms may return error codes that indicate whether the card is valid (e.g., "insufficient funds" instead of "invalid number").
   • Often, attacks are automated using bots that send thousands of requests through proxy servers to avoid IP blocking.
4. Use of valid numbers:
   • If the card number is confirmed as valid, scammers try to use it to:
     • Shopping in online stores (especially those that do not require 3D Secure or CVV).
     • Signing up for services with a trial period (e.g. Netflix, Spotify) and then reselling the accounts.
     • Transferring funds through payment systems with a low level of security.
   • Valid numbers can also be sold on the black market (for example, on the darknet for $10–50 per card, depending on the balance and type).
5. Scaling attacks:
   • To increase their effectiveness, fraudsters use card generators (programs for creating numbers) and checkers (tools for checking validity).
   • They also use data spoofing (IP, User-Agent substitution) and multi-accounting (creating multiple accounts) to bypass security systems.
   • In some cases, droppers (dummy people or accounts) are used to receive goods purchased with stolen data.

Why BIN attacks are effective​

• Scalability: Automation allows you to check thousands of numbers in a short time.
• Weak security on some platforms: Not all stores or services require 3D Secure, CVV, or two-factor authentication.
• Data availability: BIN is easy to find and the Luhn algorithm is an open standard.
• Low risk to fraudsters: Card testing often does not require full information (such as the owner's name or CVV), reducing the barrier to attack.

Examples of vulnerabilities exploited in BIN attacks​

• Payment gateways with minimal verification: Some systems verify the card without checking the balance or CVV.
• Test transactions: Services that charge $0 or $1 to verify a card can be used to confirm the validity of a number.
• Sites with poor security: Small online stores or outdated platforms may not have modern fraud detection systems.

Methods of protection against BIN attacks​

Protecting against BIN attacks requires a collaborative effort between users, businesses, and banks. Here are detailed recommendations:

For users​

1. Using virtual cards:
   • Many banks (e.g. Revolut, Monzo) offer virtual cards with single-use numbers or limits that can be used for online purchases.
   • This reduces the risk of compromising the main card number.
2. Transaction Monitoring:
   • Set up transaction alerts in your banking app to monitor for suspicious charges.
   • Check your card statements regularly.
3. Two-factor authentication (2FA):
   • Use 3D Secure (Verified by Visa, Mastercard SecureCode) for an extra level of security when making online payments.
   • Enable SMS or push notifications to confirm transactions.
4. Data limitation:
   • Don't save card details on websites, especially on lesser-known platforms.
   • Use payment systems (PayPal, Apple Pay) that tokenize card data.
5. Cyber hygiene:
   • Avoid phishing sites and emails that may request card details.
   • Use a VPN to secure your connection when shopping on public Wi-Fi.

For businesses and payment systems​

1. Strengthening transaction verification:
   • Implement 3D Secure for all online payments, requiring additional confirmation (e.g. SMS code or biometrics).
   • Require CVV and cardholder name entry for every transaction.
2. Data tokenization:
   • Replace real card numbers with tokens that are useless to fraudsters outside of a specific platform.
   • Example: Apple Pay and Google Pay use tokenization to protect data.
3. Limit input attempts:
   • Set limits on the number of attempts to enter a card number from one IP or device (for example, 3–5 attempts).
   • Implement CAPTCHA or other anti-bot mechanisms to prevent automated attacks.
4. Monitoring and analytics:
   • Use machine learning-based Fraud Detection Systems to analyze patterns (e.g., duplicate BINs, abnormal traffic).
   • Track the geolocation of requests and block suspicious IP addresses or proxies.
5. Compliance with standards:
   • Comply with PCI DSS (Payment Card Industry Data Security Standard) to protect card data.
   • Update your payment gateway software regularly.
6. Blocking vulnerable operations:
   • Disable the ability to make test transactions of $0 or $1 without additional verification.
   • Check that the card's geolocation matches the buyer's IP address.

For banks​

1. Issuance of disposable numbers:
   • Offer your customers virtual cards or disposable numbers for secure purchases.
   • Set default limits for online transactions.
2. Monitoring suspicious activity:
   • Use algorithms to detect mass attempts to verify numbers (e.g., repeated authorization requests).
   • Block cards if suspicious transactions are detected and notify the owner.
3. Customer training:
   • Conduct campaigns to educate users about the risks of phishing and carding.
   • Provide instructions on how to use cards safely online.

Real-life examples and consequences​

• Attack example: In 2020, scammers used BIN attacks to validate thousands of generated numbers on trial subscription platforms, creating accounts for resale. This resulted in financial losses for companies and a leak of user data.
• Consequences: For victims, this could mean theft of funds, temporary card blocking, or the need for a replacement card. For businesses, this could mean financial losses, reputational damage, and recovery costs.

Conclusion​

BIN attacks are a technically simple yet effective technique used by carders to exploit vulnerabilities in online payment systems. They rely on automation, data availability, and the inadequate security of some platforms. For protection, it's important to use modern technologies (tokenization, 3D Secure), monitor transactions, and practice cyber hygiene. Users are advised to be vigilant, and businesses should invest in fraud detection systems and security compliance.

If you have any additional questions or require clarification on specific aspects, please let me know, and I'll be happy to help!