The Fraudster’s Guide to Click Fraud: Tactics, Targets, and Benefits

Click fraud isn’t new to us, but it’s evolving. At its core, it’s simple: we make ad platforms believe people are engaging with ads when no one really is.

And no matter whether that engagement is through bots, misclicks, or incentivized traffic, the outcome’s the same: we trigger clicks, cash out, and move on before anyone looks too closely. The beauty is that most platforms still reward volume over quality. That’s the weakness we’ve built around.

Most platforms still reward volume over quality. That’s the weakness we’ve built around.

But basic scripts and low-grade traffic—the kind of stuff we used to be able to do for spending cash—those don’t cut it anymore. We’ve got to operate with more intelligent bots, cleaner setups, real devices, and behavioral mimicry that fool most detection layers. We’ve adapted from click farms with thousands of devices to malware-based schemes that hijack actual user sessions. And we’ve had to, because the ecosystem we exploit is getting smarter too.

What follows is a breakdown of what’s working, where the cracks still are, and how we keep ahead of the defenses. From DSPs to retail media platforms, we’ll map the weak points and show how the right mix of automation, scale, and subtlety keeps us profitable and undetected.

Let’s get into it..

The right mix of automation, scale, and subtlety keeps us profitable and undetected.

Click fraud is simple: make it look like real people are clicking on ads, even though there’s no actual interest driving that click.

And every little bit matters. Even a tiny fraction of fake clicks can add up to significant payouts when scaled across large campaigns, regardless of what mechanism is used for that click.

Even a tiny fraction of fake clicks can add up to significant payouts.

Now, impression fraud has its place, too.
It involves serving ads in ways that technically count as impressions but are never actually seen by real users, like loading ads in hidden 1×1 pixel iFrames or directing bot traffic to inflate view counts. While impression fraud can boost apparent reach, it rarely generates the kind of direct revenue that click fraud does. Clicks are just plain more expensive for the advertiser than impressions.

It’s all about volume, subtlety, and making those clicks seem convincing enough to slip past detection systems. The best attacks blend in so well that platforms never notice them at all and just chalk them up to real engagement.

It’s all about volume, subtlety, and making those clicks seem convincing enough to slip past detection systems.

Digital advertising puts a premium on engagement (as opposed to just exposure), but there’s often little scrutiny on the quality of that engagement. That’s the gap we exploit.

The system rewards clicks, and we’re here to deliver them, whether they come from a bot, a hijacked session, or a cleverly timed misclick.

Advertisers gravitate toward high-intent traffic and actions. We can supply that traffic and those actions. Between the noise of real users and the complexity of the ad serving process, it’s surprisingly easy to slip through undetected. Many of our tactics are built to mirror valid user behavior, right down to cursor movements and page load times. Some lie dormant for hours to avoid detection. Others hijack real devices, turning genuine human activity into fake conversions. Either way, the result is the same: more money in our pockets, skewed data for the advertisers, and a system none the wiser.

Here are just a few of the tools in our click fraud toolkit:

• Bots or invalid clicks
  Bots are automated scripts programmed to behave like real users, including loading web pages, moving cursors, and pausing just long enough to pass as a human interaction. Most bots are run out of data centers or through hijacked residential IPs, making them hard to trace and block. Some are basic, while others are nearly indistinguishable from actual traffic, using advanced evasion tactics like rotating user agents and IP addresses. Either way, the threat is clear: inflate click-through rates, eat into ad budgets, and never convert. Advertisers see activity, assume interest, and keep paying. All we have to do is stay just human enough to pass through the filters.
• Incentivized clicks
  Why rely on bots when you can pay humans to click for you? Incentivized traffic gets real people to engage with ads, not because they care, but because they’re earning points, unlocking content, or pocketing spare change. Reward apps and pay-to-click schemes are full of users who click anything for a payout. These aren’t high-intent clicks—indeed, they’re zero-intent clicks—but they pass fraud checks and keep the metrics looking healthy. It’s technically human traffic, just not the kind that’s buying anything. The scale can be massive, and since these users are real, it’s one of the hardest tactics for fraud detection tools to isolate.
• Accidental clicks
  Accidental clicks happen when users unintentionally tap or click an ad, usually because it’s placed too close to navigation buttons, disguised as part of the content, or timed to pop up just right. It’s not technically fraud but these misclicks still register in the data and burn through budgets. And best of all, they’re hard to trace back to intent, making them a perfect low-effort tactic in our broader strategy. After all, sometimes accidental clicks can be a feature, not a bug, especially in cases of arbitrage in the systems we’re targeting. Leveraging (deliberately) poor UX and aggressive ad placements only amplifies this effect.
• Creative verification and validation
  Not all invalid clicks come from fraud actors like us. Some come from ad scanners, verification vendors, and QA bots. These systems pre-load creative to test it for malware, content violations, and technical compliance. In the process, they trigger clicks that look real but aren’t tied to any user behavior. Unless advertisers properly tag and filter it, these benign clicks blend in with everything else, skewing campaign metrics and muddying the waters. Awareness of these legitimate but misleading clicks allows us to tailor our tactics around them, blending in seamlessly.

Not every target is the same, but nearly every part of the digital advertising ecosystem is ripe for click fraud.

Advertisers have more ways than ever to serve digital ads, which means more opportunities for us to step in. The paths may differ from demand side platforms to walled gardens and retail media networks, but the weaknesses are there if you know where to look. Their business models hinge on scale, automation, and performance, but if they’re not keeping a close eye on what’s behind the click, that’s where we come in.

Demand Side Platforms (DSPs)
DSPs are all about efficiency at scale. Their algorithms optimize campaigns in real time based on click and conversion signals. They will adjust budgets and bids based on bad data because of fraudulent noise, through bots, farms, or fake intent. The faster they optimize, the faster we profit. And since DSPs are built to move fast, many only catch suspicious patterns after the fact. If their fraud controls aren’t sophisticated, clicks that look clean can slip right through and reroute serious budget before anyone blinks. The fragmented nature of programmatic supply chains also means fraud can hide behind layers of intermediaries, increasing the challenge of detection.

Walled Gardens
Walled gardens, including some of the highest-profile advertising platforms available, promise high-quality inventory and performance based on their closed (walled) ecosystems. They have the user data, the scale, the advertising technology, and the responsibility. Brands trust them to deliver results that feel premium and performance. However their closed nature can work in our favor: limited visibility and restricted third-party measurement can make it easier for low-quality or manipulative clicks to hide in plain sight. Advertisers may never see the cracks if a click looks legitimate inside the walls. As a reminder, even the most advanced platforms can’t always share enough data to expose fraud fully.

Commerce Media Networks (CMNs)
CMNs are the rising stars of the ad world, connecting media spend directly to consumers’ purchase behavior. That’s great for everyone except us. They are performance-driven, and when they are well-run, click fraud is more complex to get away with. But not impossible. Many CMNs are still maturing; if their fraud detection doesn’t evolve alongside their growth, we can still find gaps. Sponsored listings, native placements, and on-site banners create click paths we can exploit, especially if attribution is only loosely tied to shopper behavior. As these networks grow rapidly, they can struggle to keep fraud controls up to speed.

For now, we’ve been able to exploit the fragmentation. Platforms are focused on their own metrics, their own buyers, their own fraud controls. But things are shifting. We’re seeing more collaboration between advertisers, platforms, publishers, and click verification tools. The smarter they get, the tighter the loop becomes. Shared standards, better measurement, and clean signals across the chain all threaten our success.

Click fraud isn’t just about siphoning off ad dollars. It’s about throwing a wrench into the entire system and watching it malfunction in real time. One well-executed click fraud attack doesn’t just affect a single campaign, it ripples outward, distorting data, wrecking optimizations, and forcing advertisers to second-guess every decision. In a digital ecosystem that depends on clean signals, all we have to do is pollute the data and let the system sabotage itself.

Campaign metrics go sideways
Start with the numbers. Click data feeds everything from budget allocation to performance forecasting. Advertisers make decisions based on junk once we get in and start skewing those numbers. Publishers think they’re delivering results. Platforms think their targeting is working. Meanwhile, nothing’s converting, and no one knows why. We’re not just breaking dashboards, we’re breaking the trust behind them. This kind of confusion slows down marketing efforts and creates long-term skepticism that benefits no one but us.

Budgets burn fast
It doesn’t take much to start draining ad spend. A few thousand clicks from bots or incentivized users can look legitimate enough to pass the checks, but they won’t generate a single sale. Multiply that across millions of impressions, and the waste adds up fast. Advertisers start bleeding money, CPC/CPA metrics spike, and channels that should be performing get sidelined because we’ve made them look broken. Over time, these wasted dollars can lead to budget cuts or changes in strategy that limit overall market growth, decisions that are made because of bad data rather than actual performance.

Optimization goes off track
All that bad data gets fed right back into machine learning models. Platforms think they are optimizing but are doubling down on poor performing inventory. Budget flows to placements we have flooded with fake traffic. Audiences that never convert get more attention. Over time, the algorithms get dumber, not smarter, and we don’t even have to continue after the first wave. The compounding effect means our initial efforts continue paying off long after we are gone.

Trust erodes across the board
Eventually, the questions start: why isn’t this campaign performing? Why is the return so low? Why does everything look right, but feel off? That’s when advertisers start pulling back. They get cautious. They demand more verification. They move budget into walled gardens or closed platforms. That fragmentation works in our favor too. It makes collective defense harder, and creates more gaps for us to exploit elsewhere. The ecosystem becomes fractured, less efficient, and more vulnerable to our next moves.

Click fraud might start with a single bad click, but it ends with fractured budgets, confused machines, and an industry constantly chasing shadows. For us, chaos is well worth the effort.

For a while, click fraud was easy money. All we had to do was fake the right signals, slip past the filters, and let the system do the rest. But that’s changing.

HUMAN’s Ad Click Defense is making it harder to hide. They are not just looking at devices or endpoints, they are watching the click itself.

Their tech analyzes real-time click behavior across a massive footprint, pulling from 20 trillion weekly interactions and more than 3 billion unique devices a month. That kind of visibility makes it tough for our tricks to hold up. Bots that used to blend in get flagged. Incentivized traffic starts looking suspicious. Even our more advanced setups can’t mimic true human intent well enough to pass.

And the impact? Platforms regain control. Advertisers trust their data again. Optimization engines run on clean signals. With HUMAN in place, our advantage disappears, and suddenly, all those fraudulent clicks don’t lead anywhere.

If more of the ecosystem adopts defenses like this, our opportunities shrink fast. Ad Click Defense is one of the few tools that can shut us down.