How Honeypotting Breaks the Feedback Loop for ATO and Credential Stuffing

One of the most underutilized strategies in fraud defense today is honeypotting, not just detecting malicious behavior after the fact, but actively confusing and derailing fraudsters in the moment.

At Spec, we’ve developed a way to “mock the message,” to intercept attacker behavior like credential stuffing, ATO, and card testing before it becomes a business problem. Rather than giving fraudsters the information they’re looking for, we hand them a pile of noise.

Let me explain.

Attackers Learn From Failure. That’s the Problem.

In most environments, attackers learn quickly. Failed login attempts, registration issues, and payment declines offer a wealth of signals, especially when those errors are specific:

• “Username doesn’t exist”
• “Incorrect password”
• “Card declined: insufficient funds”

These messages might seem harmless, but each one reveals something about the system. Attackers can test credentials at scale and get real-time feedback to validate lists of usernames, passwords, or stolen cards. Even if a login fails, it can still confirm that an account exists. That’s value they can resell.

When attackers leverage AI-assisted bots or other machine learning tools, they adapt rapidly. If you're feeding an AI model clear signals about what's working and what's not, it can optimize for success far faster than a human ever could. At Spec, we’ve identified AI-driven attack behavior by observing how quickly these bots evolve during honeypot engagement, adjusting their inputs in real time in an attempt to break through. But with our dynamic honeypotting in place, even AI can’t find a reliable path forward.

Error Messaging Breaks the Loop

With Spec, we present standard, custom, or randomized failure messages that remove useful feedback. Fraudsters don’t know that they failed or why they failed. Was the username wrong? The password? Does the account exist? Did we detect a bot? Were they prevented by the system?

They’re flying blind.

This forces them to rely on testing external signals, but we’re watching that behavior too, sending those new attempts through the same honeypots as their previous ones. Even if they succeed once, they don’t necessarily know and can’t replicate it as easily. Their only true signal is a successful attempt, which is rare, and with Spec introducing a blend of real and honeypot responses, attackers can no longer distinguish between genuine success and carefully crafted traps. This not only confuses a human’s ability to figure out what is going on, but it hinders AI assistance from doing so, too. We're making them work harder and get less value in return.

Honeypotting at the Point of Attack

Other solutions might log these attempts and provide post-attack signals, but that’s too late. With Spec, honeypotting happens at the fact, not after it. We can trigger fake login pages, fake error messages, and controlled flows that look real to the attacker but give them nothing useful.

We deployed this with one of our customers to protect their login and registration flows. Now, attackers think they're making progress, but they're stuck in a loop of dead ends. No signal. No learning. No resale value.

Attacks like this, and many others we’ve mitigated using these methods, are ending faster than anything I’ve seen from traditional vendors. What typically takes three months to show measurable decline now takes just days. Unlike conventional solutions where traffic drops gradually as attackers shift tactics, Spec’s honeypots cut off the value entirely. There's no workaround. The traffic doesn’t taper off, it stops. Period.

One of our clients was facing thousands of card testing attempts per day before turning to Spec for help. Their payment processor responses were unintentionally providing detailed feedback to fraudsters, making the platform highly attractive for card testers eager to validate stolen credentials and understand failure reasons.

To combat this, we implemented our proven methods, including advanced honeypotting techniques, to intercept malicious activity before it ever reached their infrastructure. At the same time, we masked processor responses to prevent any valuable signals from being exposed.

Within a short period, card testing fraud plummeted by 98%, and malicious traffic fell just as dramatically. By eliminating the value attackers were extracting from failed transactions, Spec rendered the platform useless to them, so they packed up and moved on.

What This Means For Fraud Fighters

For fraud teams, this means fewer successful attacks, fewer chargebacks, and a dramatic reduction in noise from credential stuffing campaigns and automated registrations. You don’t just detect more—you prevent more.

For engineering and product teams, honeypotting doesn’t require ripping out existing infrastructure or building custom logic. You don’t have to design your own obfuscation strategy. With Spec, you can introduce randomized responses, traps, and misleading flows without touching production code.

Why It Matters Now

Credential stuffing and account takeovers are part of a thriving economy built on stolen data and automated attacks. In 2023 alone, over 10 billion credential stuffing attempts were detected, a nearly 20% increase year over year. Meanwhile, more than 85% of MRC members report being targeted by card testing, and global credit card fraud losses are projected to hit $43 billion by 2026.

Every message your system shows is a lesson for the attacker. Honeypotting takes away their teacher.

Want to see how it works in your environment? Let’s talk honeypots.