Spike in carding attacks puts ecommerce retailers at risk

Carding attacks typically progress through five stages, beginning with stolen credit card data sourced from breaches or phishing, then targeting vulnerable online retailers—especially those with guest checkouts and exposed payment systems lacking robust fraud controls

Online retailers in the United States have seen a dramatic 140% increase in credit card fraud over the past three years. A significant portion of this rise is attributed to carding attacks, where cybercriminals test stolen credit card details en masse to identify valid payment methods. These attacks pose a severe threat to ecommerce merchants, disrupting operations and eroding customer trust.

What makes carding particularly dangerous is its sophisticated execution. Fraudsters use human-assisted bots and artificial intelligence to mimic legitimate user behaviour, bypass security defences, and adapt in real-time to avoid detection. While the traffic may appear normal initially, the consequences of these attacks are far-reaching. They lead to chargebacks, distorted analytics, and performance degradation, all of which can harm businesses and compromise customer relationships.

How carding attacks work

Carding attacks generally unfold in five key stages. First, cybercriminals acquire stolen credit card data, often sourced from dark web marketplaces, data breaches, or phishing campaigns. With this stolen data, attackers target online retailers, particularly those with exposed payment systems or sites that allow guest checkouts. These sites are especially vulnerable as they tend to have weaker fraud controls.

The next step involves testing the stolen cards. Fraudsters make small purchases to verify if the cards are active and functional. Once they find a working card, they either use it for larger fraudulent transactions or sell it on the black market. If a card fails, the attacker discards it and moves on to the next one, continuing the cycle.

Signs of a carding attack

Detecting a carding attack can be difficult because it often mimics regular user traffic. However, there are several indicators that may point to fraudulent activity. A sudden increase in failed payment attempts, particularly around specific products or geographic locations, is one warning sign. Other signs include a rise in low-value transactions over a short period, increased activity via guest checkout options, and a notable uptick in chargebacks or alerts from payment processors. High bounce rates from checkout pages may also indicate that automated testing scripts are abandoning carts after failed transactions.

Advanced fraud detection with behavioral analytics

To combat these sophisticated fraud tactics, many ecommerce platforms are turning to behavioral analytics and machine learning. These systems monitor and assess user activity throughout the entire customer journey, not just during login. By establishing a baseline for normal behavior, these tools can detect anomalies in real time and respond accordingly.

Behavioral signals that are commonly monitored include device and browser characteristics, email and phone number patterns, IP reputation, and unusual checkout behaviors. Systems use these signals to assign dynamic risk scores to users, allowing businesses to take immediate action—such as requiring additional verification, issuing alerts, or blocking suspicious requests.

Real-world cases of carding detection

Two real-world examples highlight the effectiveness of behavioral analytics in detecting carding. In one case, a U.S.-based grocery retailer saw suspicious checkout activity. By analyzing user behavior, the system detected over 3,000 attempts from 45 unique user IDs in just two weeks, which matched typical carding behavior. The retailer was able to block these attempts before any damage was done.

In another example, a major retailer faced a surge in card validation requests, each carrying financial costs. Fraudsters were testing large numbers of stolen cards, risking both monetary loss and penalties. The retailer’s behavioral system flagged the activity and automatically initiated CAPTCHA challenges and request blocking, reducing fraud and saving operational costs.

The importance of real-time risk assessment

Traditional static fraud detection rules are no longer sufficient to combat evolving threats. A dynamic, risk-based approach that adapts in real time is crucial for ecommerce merchants. By understanding user behavior and intent, businesses can stop fraud at its earliest stages while minimizing disruptions to legitimate customers.

Protecting the entire customer journey

As fraud tactics become more advanced, ecommerce platforms must rely on intelligent, adaptive security systems that monitor user behavior throughout the entire customer journey—from account creation to checkout. By leveraging real-time behavioral analytics and contextual risk scoring, businesses can protect themselves from carding attacks and ensure a seamless, secure experience for their customers.