Carding Attacks

Carding attack bots are automated tools used to test stolen credit card details on checkout pages. Attackers typically attempt small purchases (micro-transactions) to verify whether the card details are valid before using them for larger fraudulent transactions. These bots exploit vulnerabilities in payment systems, often targeting websites with weak security measures or lacking robust fraud detection. Carding attacks are particularly harmful to e-commerce sites, digital service providers, and donation platforms, where automated transactions can go unnoticed until significant damage is done.

Impact

Carding attacks can have severe financial and reputational consequences for businesses. Websites targeted by these attacks often face payment processor penalties, chargeback fees, and legal risks. If fraudulent transactions are detected in large volumes, businesses may be blacklisted by payment gateways, making it difficult to process legitimate payments. Additionally, a surge in bot-driven transactions can overload servers, disrupt genuine customer checkouts, and increase operational costs.

Example

An online store notices an increase in failed transactions with small amounts, such as $1 or less. This is a sign that carding bots are testing stolen credit card details. Once a valid card is identified, attackers proceed to make larger purchases or sell the verified details on the dark web. As a result, the store faces a high volume of chargebacks, disputes with payment providers, and a potential suspension from its payment gateway.

Mitigation

To protect against carding attacks, businesses implement fraud detection systems, transaction velocity checks, and CAPTCHA challenges on checkout pages. IP reputation analysis and device fingerprinting can help identify and block malicious bot traffic. Additionally, enabling 3D Secure (3DS) authentication and setting transaction limits for new or unverified users reduces the likelihood of successful attacks. Regularly monitoring payment logs for unusual patterns, such as a high volume of small transactions, is also essential for early detection.