What Is a BIN Attack? Understanding the Threat and How to Respond

BIN attacks are one of the most common forms of automated credit card fraud—and they’re growing in scale and sophistication. You may have heard of them through recent reports involving compromised merchants, unauthorized charges, or spikes in chargebacks. But what actually is a BIN attack, and what should businesses be doing to detect and prevent them?

Let’s break it down.

🧠 What Is a BIN?

A Bank Identification Number (BIN) is the first 6–8 digits of a credit or debit card. It identifies the card issuer and offers basic details about the card—such as whether it’s a Visa or Mastercard, which country it was issued in, and whether it’s a credit, debit, or prepaid card.

Every payment card has a BIN, and fraudsters use this structure to launch what’s known as a BIN attack.

🚨 What Is a BIN Attack?

A BIN attack is a brute-force technique where fraudsters use software to guess valid combinations of:

Card numbers (based on known BIN ranges)
Expiration dates
CVVs

These attacks don’t involve hacking into bank systems or breaching databases. Instead, they rely on high-speed automation to generate and test combinations across online payment forms.

Fraudsters may target merchants with minimal fraud controls or test cards with small transactions—often invisible to the cardholder. Once they find a working combo, they either:

Use it to make fraudulent purchases
Sell the data on illicit marketplaces

🎯 Who’s Being Targeted?

In reality, every cardholder is vulnerable. BIN attacks aren’t targeting specific banks, card types, or loyalty programs. If your card shares a BIN range with other cards—and most do—it’s potentially exposed.

In recent cases, cards from multiple issuers were affected, including the Wells Fargo-issued Bilt Mastercard. Fraudsters focused on one BIN range at a time, hitting “trusted” merchants to avoid detection.

“They use compromised merchants to randomly test millions of potential card numbers to see which ones work.”
— Statement from Bilt

This has been observed across banks, platforms, and merchant types.

🔎 What Makes BIN Attacks So Dangerous?

They don’t require a data breach. No hacking is involved—just educated guesses and speed.
They're automated. Thousands of attempts can happen in minutes.
They’re hard to detect. Many test attempts fail silently, and successful ones may be small enough to avoid scrutiny.
They can happen across multiple sites. Once a card is validated, it might be used on a different platform entirely.

🛡️ How Can Businesses Detect and Prevent BIN Attacks?

Stopping BIN attacks starts with strengthening your fraud detection systems before authorization. Two proven tools help:

✅ 1. Instant BIN Lookup API

This API gives real-time access to:

Card brand and type
Issuing country
Prepaid or commercial status
6- or 8-digit BIN expansion

With this info, you can:

Block or flag risky BIN ranges
Set dynamic rules by region or card type
Detect mismatched geo-IP vs. BIN country
Limit how many card attempts are allowed from the same BIN

✅ 2. ML-Powered Fraud Detection API

Use real-time machine learning scoring to:

Detect brute-force bot patterns (velocity, session anomalies)
Score transactions based on card details, device fingerprint, IP, and behavior
Auto-decline high-risk patterns before they reach settlement
Reduce false positives with smarter, context-aware decisions

🔒 What Can Cardholders Do?

While businesses play a key role in stopping BIN attacks, consumers need to be alert as well:

Regularly check credit card activity for small or suspicious charges
Set up fraud alerts via bank email, app, or SMS
Lock your card if you suspect exposure, but understand some charges (e.g., refunds, recurring bills) may still go through
Request a new card number if fraud is confirmed
Report any unauthorized activity immediately to your issuing bank—not the loyalty program or retailer

⚙️ What Should Fintechs and Merchants Prioritize?

BIN attacks aren’t going away. If you accept card payments and allow user input of card data, make sure your fraud stack includes:

Real-time BIN lookups to enrich card data
Rate-limiting and velocity checks for repeated attempts
Machine-learning fraud scoring that adapts to new patterns
Bot detection and behavior analysis for checkout flows
Clear incident response plans for fraud escalation

Final Word

BIN attacks are fast, quiet, and increasingly common. They bypass traditional perimeter defenses by exploiting the structure of how cards work. But with the right combination of data enrichment and machine learning, you can identify and stop these attacks early—before they become chargebacks or customer complaints.

Start with:

Protect your platform, your customers, and your bottom line.