When Attackers Build Better Products Than Startups

In cybersecurity, most of what we, as defenders, see is industrialized crime.

The act of building a phishing campaign today is not based on craftsmanship, it’s mass production. Template kits are easily bought and sold on underground markets, giving relatively unsophisticated criminals access to “minimum viable deception”. These kits aren’t fancy. They provide just enough UX to steal credentials, drain a wallet, or redirect a payment.

Most of these campaigns are disposable. The infrastructures supporting the campaigns are cheap, and the related domains are even cheaper. These domains are burned through quickly and then discarded. There is no love for the product and no pride in the build.

But every once in a while, we see a jewel.

When we do find an expertly-crafted campaign, it forces us to confront a paradox we rarely talk about: some attackers are building products that, if used for good, would outperform many legitimate startups.

The story I’m about to tell started the way many do at BforeAI.

A few weeks ago, our PreCrime platform flagged a growing cluster of phishing campaigns targeting multiple global brands. On the surface, it looked quite typical, featuring the usual impersonated domains, familiar templates, and cloned checkout pages.

But, at second glance, something stood out in our model.

This campaign was complex; it featured different brands in different verticals, addressing global audiences in different languages, and all of it was pointing to the same backend infrastructure.

When you operate at graph scale, patterns emerge that no human analyst would ever see manually. This campaign was too consistent, too clean, and too engineered to be random.

So we followed the edges of the graph.

What We Found Was Not a Phishing Kit

You might be asking what made this campaign unique? Instead of the usual plug-and-play credential-harvesting pages, what we uncovered proved to be a fully operational fake ecommerce platform.

This wasn’t a simple single-brand scam. It was a multi-brand marketplace – and a well-done one, at that.

The campaign didn’t use a static catalog. Instead, it featured a dynamic product engine continuously scraping inventory from real marketplaces, normalizing the product data, pulling specs from legitimate retailers, and keeping pricing aggressively tuned for conversion.

This platform was fully internationalized, meaning it delivered localized user experiences in multiple languages, supported multiple currencies, and dynamically adapted its pricing and checkout flows based on geography.

Behind the scenes, the infrastructure scaled automatically with traffic. When traffic to their sites increased, the infrastructure scaled up and when traffic dropped, it scaled back down. This was not a fragile scam site running on a $5 VPS.

Even the product imagery was engineered. Product photos were programmatically watermarked with brand overlays to increase perceived legitimacy and reduce friction in the purchase decision.

Then there was the growth engine.

We’re talking about a full-on ecommerce marketing campaign. The attackers were running autonomous Instagram campaigns, generating traffic, testing creatives, and feeding a full acquisition funnel into the fake marketplace.

At this point, it stopped looking like fraud.

It started looking like a startup.

The Shopify that Shouldn’t Exist

At some point during the investigation of this campaign, someone on the team said out loud, “If these guys went legit, they’d be a serious Shopify competitor.”

The criminals had done something really impressive:

• They had built a global commerce platform.
• They had solved internationalization.
• They had solved catalog aggregation.
• They had solved infrastructure scaling.
• They had solved growth automation.
• They had solved conversion optimization.

This is exactly what venture-backed founders spend years trying to build.

Except, for specific reasons we may never know, these builders chose crime.

The Real Waste in Cybercrime

We usually talk about cybercrime in terms of victim impact: the financial losses, brand damage, and erosion of trust. After all, this IS the primary negative effect of these criminal activities.

But this story highlights another dimension that deserves attention.

The waste of talent.

Whoever built this platform understands distributed systems. They understand ecommerce funnels. They understand scraping, data normalization, catalog management, pricing engines, and growth marketing. They understand how to operate globally.

This is not the work of amateurs, this is elite product engineering.

And it is being used to steal instead of build.

Why This Matters for Defenders

Stories like this force a shift in mindset. The sophisticated attackers we engage with are not just criminals. They are product teams that perform all of the same actions as a smart startup:

• They iterate.
• They run experiments.
• They optimize funnels.
• They build growth loops.
• They automate infrastructure.

Which means cybersecurity is no longer just about blocking malware. It is about competing with highly sophisticated criminal product organizations.

This is why predictive, graph-based intelligence matters. Because by the time a platform like this goes live, it is already optimized. We have to get in front of it.

A Message to the Builder Behind the Keyboard

So to the person who built this campaign, if you ever read this:

You could build a unicorn.

You clearly know how to build global platforms, scale infrastructure, automate growth, and ship real products.

You chose the wrong market.

And that is the real tragedy.

In our journey building PreCrime systems, we see the future of cybercrime forming in real time. We see how attackers iterate, adapt, and innovate faster than most companies.

And sometimes, like in this example, we see brilliance.

This story is shared not to glorify attackers, but to remind the industry what we are really up against. We aren’t battling scripts, bots, or kids tinkering in basements; we are facing motivated world-class product and engineering teams operating in the shadows.

If we want to protect the digital economy, we must build better products than they do.

And we must build them for good.