The process of checking the validity of stolen credit cards in carding: a detailed educational overview

[Student]

Carding is a form of cybercrime in which criminals use stolen credit or debit card information to commit fraudulent transactions, such as purchasing gift cards, goods, or services. This process involves several steps, including card validation to ensure their functionality. It's important to emphasize that carding is illegal in many countries, including Russia and the United States, and falls under statutes covering fraud, identity theft, and cybercrime. This answer is provided for educational purposes only, to help understand the mechanics of cyberfraud and the importance of security measures. It is based on publicly available cybersecurity sources and does not provide instructions on how to commit illegal activities. For further information, it is recommended to consult reports from organizations such as Visa, Mastercard, or cybersecurity websites.

1. General context of carding and data sources​

Carding often begins with obtaining card data through various channels: phishing (fraudulently obtaining data), hacking store or bank databases, skimming (installing devices on ATMs to read cards), or purchasing "dumps" (sets of card data) on dark markets. This data includes the card number (PAN, or Primary Account Number), expiration date, CVV/CVC (a 3-4-digit code on the back), and sometimes additional details such as the cardholder's name, address, or PIN. Experts estimate that such data sells for a few dollars per card, depending on the quality and country of issue.

Fraudsters (carders) do not immediately use cards for large purchases to avoid being blocked. Instead, they conduct validation checks to weed out "dead" cards (blocked or expired). This step is automated to process large volumes of data and minimize risks.

2. Detailed steps of the validity verification process​

The verification process can be divided into several phases, each aimed at confirming that the card is active, has funds, and does not raise any suspicions from the bank. Here's a detailed overview:

• Pre-filtering data:
  • Luhn algorithm and format verification: This is a mathematical formula for verifying the card number's checksum. The algorithm works like this: starting with the rightmost digit, every second digit is doubled (if the result is >9, the digits are summed); then all digits are summed. If the result is a multiple of 10, the number is valid. This doesn't check the actual card activity, only its structural correctness. Legitimate tools for this purpose are used in e-commerce to verify data entry, but fraudsters use them to filter out stolen lists.
  • BIN (Bank Identification Number) Analysis: The first 6-8 digits of the number identify the issuing bank, card type (credit/debit), brand (Visa, Mastercard, etc.), and country. This helps determine whether the card is suitable for certain transactions (for example, US cards may not work in Europe without additional verification). BIN analysis also reveals whether the card is premium (with a high limit).
• Testing card activity:
  • Microtransactions (card testing or card stuffing): Fraudsters attempt to make small payments (from $0.01 to $2) on sites with poor security, such as donation platforms or subscription services. If the transaction goes through, the card is considered "live." If rejected, it's considered "dead." This is done en masse using bots to simulate thousands of attempts. Banks detect such patterns through monitoring systems, blocking cards if suspicious activity is detected.
  • Pre-authorization holds: A pre-authorization request without actually debiting funds. This simulates a balance check: the bank reserves the amount but does not withdraw it. If the hold is successful, the card is valid.
  • Checking balances and limits: In rare cases, this can be done by simulating requests to banking APIs or using services that indirectly disclose information (for example, attempts to register for services that require card verification).
• Bypassing security systems:
  • Fraudsters disguise their activity to avoid detection. This includes using proxy servers or VPNs to change the IP address, mimicking the cardholder's location. They also employ "anti-detection" techniques, such as changing the device's digital fingerprint (browser fingerprint), including the user agent, screen resolution, and time zone. Modern banks use AI to analyze behavior: unusual locations, transaction frequency, or device type trigger flags.
  • Additional measures: Using virtual machines or emulators to create "clean" sessions to avoid communication between tests.
• Classification and further use:
  • After testing, the cards are sorted: "live" cards are for sale or use, while "dead" cards are discarded. Valid cards are used to purchase gift cards (to launder funds) or goods delivered to fictitious addresses. The process is often cyclical: if a card is blocked, the data is updated.

This process is evolving: with the introduction of 3D Secure (additional verification via SMS or app), tokenization (replacing real numbers with tokens), and biometrics, carding is becoming more complex.

3. Technical tools used in the process​

Validity checking tools are often automated systems adapted from legitimate technologies (for example, for testing payment systems). In carding, they are used illegally. Here are the categories at a high level (without specific names or download links):

• Validators/checkers: Programs or scripts that automate tests via payment gateway APIs. They check Luhn, BIN, and process microtransactions. Legitimate analogs are used by developers for e-commerce testing, but fraudsters modify them for mass processing.
• BIN analyzers: Tools for parsing the first digits of a number, identifying the issuer and type. Available online for legal use (checking input in forms), but for filtering in carding.
• Bots and automation scripts: Written in languages like Python or JavaScript, they simulate browser sessions for testing. They use libraries for HTTP requests. In a legal context, they are used for automated website testing.
• Anonymizers (proxies, VPNs, anti-detection browsers): Services for masking traffic. Anti-detection browsers change device settings to avoid fingerprint blocking.
• Test data generators: Legitimate tools create fake numbers for development, but scammers can use them to train systems.

These tools are often distributed on underground forums, but their use for fraud is punishable by law.

4. Prevention and detection measures​

Banks and payment systems are actively combating carding:

• AI-based fraud detection systems: Analyze patterns (e.g., many small transactions from one location).
• PCI-DSS standards: Require data encryption and regular audits for merchants.
• Tips for users: Use virtual cards, monitor transactions, enable two-factor authentication.

For in-depth research, I recommend resources from the FBI, Europol, or sites like Krebs on Security. If you have any questions about cybersecurity, please ask!