An Introduction to Carding and the Role of Automation
Carding is a type of cybercrime in which criminals (carders) use stolen payment card data to conduct fraudulent transactions. This involves not only stealing the data but also verifying (testing) it for validity to ensure the card is active and has a sufficient balance. In 2025, carding remains one of the most prevalent financial cybersecurity threats, costing the economy billions of dollars annually. According to reports from companies like Verizon and Chainalysis, the market for stolen card data has grown by 20-30% in recent years, largely due to the automation of processes using bots.Automation with bots allows carders to scale operations: instead of manually entering data on websites, bots perform thousands of checks per hour, minimizing time and risk. This is especially relevant as banks and payment systems implement increasingly sophisticated security measures, such as 3D Secure and machine learning for anomaly detection. For educational purposes, it's important to understand how this works at a conceptual level to understand system vulnerabilities and preventative measures. We'll cover the process at a high level, without going into technical details that could be exploited for malicious purposes.
How Carders Obtain Card Data: Testing Prerequisites
Before turning to bots, it's worth understanding the source of the data. Carders don't always steal cards themselves; they often purchase "dumps" (lists of data) on underground markets. In 2025, markets such as those on the darknet (for example, through Telegram bots or specialized forums) offer dumps for between $1 and $100 per card, depending on quality (full data, including CVV and address, is more expensive). Data sources include:- Massive breaches: Hacking of retailer or payment processor databases. For example, in 2024–2025, major incidents similar to the Ticketmaster attack were recorded, affecting millions of users.
- Phishing and malware: Fake websites or malware (such as infostealers like RedLine) that capture data as you type.
- Skimming: Physical devices at ATMs or POS terminals, although this is less common in 2025 due to EMV chips.
- Bin attacks: Generating card numbers based on the BIN (the first 6 digits identifying the bank), combined with brute-force attacks on the CVV.
The data is often incomplete — for example, missing a CVV or expiration date — so bot testing involves "cracking" (recovering) these elements.
The Role of Bots in Automation: A Conceptual Overview
Bots are software scripts that mimic human behavior online. They are written in languages like Python or JavaScript and use libraries for browser automation (for example, to emulate clicks and inputs). In carding, bots solve the problem of scale: manually testing a single card takes minutes, but bots process thousands in parallel using cloud servers or botnets (networks of infected devices).The main steps for using bots for testing:
- Infrastructure preparation:
- Carders set up bots using proxy servers (to change IP addresses) and VPNs to avoid geolocation or IP blocking. In 2025, "residential proxies"—IP addresses from real devices that appear to be regular users—will be popular.
- Integration with CAPTCHA solvers: Bots use services where humans or AI solve CAPTCHA for a fee (from $0.001 per CAPTCHAs) to bypass website protections.
- Loading data: The bot reads a dump file (CSV or TXT) containing thousands of lines of card numbers.
- Selecting targets for testing:
- Bots focus on websites with a low threshold of suspicion: e-commerce platforms (for example, small online stores), subscription services (like Netflix), donation forms (on streaming platforms), or gift card sites.
- In 2025, the trend is "silent testing": Instead of making real purchases, bots add cards to a website wallet, checking their validity without debiting funds. This reduces the risk of bank notifications.
- Testing process:
- Transaction simulation: The bot automates the steps of registering an account, adding an item to the cart (cheap, $1–$10, so as not to attract attention), entering card details, and attempting payment.
- Error handling: If a transaction is declined, the bot analyzes the error code (e.g., "insufficient funds" vs. "invalid CVV") and tries variations. For incomplete data, brute-force is used: trying all possible CVVs (000–999) or dates (MM/YY).
- Parallelism: Bots run in multiple threads - one bot can test 50-200 cards simultaneously, with pauses to simulate human behavior (random delays).
- Adaptation to protections: Modern bots integrate AI to recognize changes on websites (for example, updating payment forms) and bypass behavioral analysis (device fingerprinting).
- Post-processing of results:
- The bot logs "live" (valid) cards into a separate file, with details such as balance (if the site allows checking).
- Successful cards are used for monetization: purchasing goods for resale, cashing out through gift cards or crypto exchanges.
- In 2025, bots are often combined with ML models to predict success based on historical data (for example, cards from a certain bank are more likely to be approved).
High-level technical aspects:
- Languages and tools: Bots are built on web automation frameworks that allow headless browser control. They integrate payment gateway APIs for simulation.
- Scale: Large-scale operations use botnets (thousands of devices) controlled through C&C servers. It's estimated that a single carder with a bot can earn $10,000–$100,000 per month.
- Evolution in 2025 With the advancement of AI, bots are becoming "smarter" — they analyze real user traffic and replicate it (mouse movements, keystrokes). Mobile bots for app emulation are also popular.
| Aspect | Description | Benefits for carders | Risks and countermeasures |
|---|---|---|---|
| Speed | Testing 1000+ cards/hour | Effortless scale | Banks block based on request frequency; solution: rate limiting detection |
| Anonymity | Proxy + fingerprint spoofing | Banov bypass | Device fingerprinting (Canvas, WebGL); solution: AI-based fraud detection |
| Price | Bots cost $50–500 on forums | Availability | Legislation (e.g., CFAA in the US); solution: police reports |
| Efficiency | Brute-force + logging | High yield (10–30% valid) | Chargebacks; solution: merchant monitoring |
Consequences and ethical aspects
- For victims: Financial losses (average losses of $200–$500 per card), loss of trust in online payments, and the need to replace cards. In 2025, psychological stress from suspicious activity notifications will increase.
- For businesses: Chargeback losses amount to 1.5% of revenue. Websites lose reputation, and insurance premiums rise. According to the Nilson Report, global losses from carding will exceed $40 billion by 2025.
- Social consequences: Carding finances other crimes (drugs, hacking). In developing countries (Russia, Nigeria), it's a "job" for young people, but it leads to arrests (for example, FBI operations like "Cardplanet" in 2024).
- Legal: In most countries (including Russia and the US), carding is a felony, with sentences of 5–20 years. Interpol coordinates arrests.
How to Protect Yourself: Educational Recommendations
Understanding the mechanism helps with prevention. For individuals:- Use virtual cards (one-time) from banks like Apple Pay.
- Enable 2FA and transaction notifications.
- Monitor your credit history (services like Credit Karma).
- Avoid suspicious websites; check HTTPS and reviews.
For business:
- Implement tokenization (replacing card data with tokens).
- Use AI fraud detection (from companies like Riskified).
- Monitor patterns: a high number of declines is a sign of a bot.
- Cooperate with PCI DSS for compliance.
In conclusion, automation with bots makes carding effective, but also vulnerable to countermeasures. Education is key to risk mitigation: knowledge helps developers and users build more secure systems. If you have specific security questions, please ask!