Also known as organics poaching, click spam is a type of advertising fraud that happens when a fraudster executes clicks for users who haven’t made them. Unless preventative measures are in place, this allows fraudsters to claim credit for fake clicks. Click spam begins with a user landing on a mobile web page or in an app which a fraudster is operating. From there, several kinds of fraud could take place: The mobile web page could be executing clicks in the background without visible ads. The spammer could begin clicking in the background while the user engages with their app, making it look as though they have interacted with an advertisement The fraudulent app can generate clicks at any time if they run an app that is active in the background 24/7 (e.g., launchers, memory cleaners, battery savers) The fraudster could send impressions-as-clicks to make it look as if a view has converted into an engagement . The spammer could send clicks from fraudulent device IDs to tracking vendors. What unites these approaches is that a user is not aware they’ve been registered as interacting with an ad. This is because, in actual fact, they never saw an ad. The fraudulent scheme occurs in the background of the user’s device while they stay none-the-wiser.

Why is click spam important?

Click spam , click injection and SDK spoofing are three common threats that steal marketing spend and ruin the accuracy of a marketer’s data. Click spamming captures organic traffic, brands it without its knowledge and then claims the credit for those users. It is a fraudulent technique that occurs on a massive scale, presenting an all-too-common problem within the mobile industry. Click spam skews valuable metrics marketers need for accurate decision-making, affecting datasets and rendering them unreliable – underplaying the impact of marketing that could have generated organics . Click spam also threatens the certainty of acquisition decisions. If an advertising network is claiming organic users and these users perform well within an app, the advertiser may decide to invest in that channel in order to acquire more of the same type of users. This creates a negative loop where the advertiser continues to pay someone else for the users they would have already acquired organically (or at least through other marketing channels). This continues until they realize the mistake – by which point it’s too late to change those strategic decisions. To understand the difference between click spam and click injection fraud, take a look at our guide to these terms, here . As leaders in fraud prevention, Adjust also offer the Fraud Prevention Suite as an additional package, preventing fraudsters from stealing your ad spend and compromising your data.

What is click spam?

Click spam is a form of mobile ad fraud that logs fake ad clicks before a real install, leading to incorrect attribution and wasted marketing spend.

Glossary

What is click spam?

Also known as organic poaching, click spam is a type of mobile advertising fraud that happens when a fraudster executes clicks for users who haven’t made them. The fraudster's goal is to claim credit for app installs they didn’t influence, often installs that would otherwise be considered organic, and receive advertising payouts. With proper detection tools, click spam can be identified and blocked before it impacts attribution.

How click spam works

Click spam works by sending fake ad clicks to attribution platforms, making it appear that a user tapped on an ad when they didn’t. These clicks are created programmatically and sent as tracking requests that include metadata like device ID, IP address, timestamp, and campaign ID. To attribution systems, they appear legitimate, even though no ad was shown and the user took no action.

Flowchart showing how click spam sends fake ad clicks to attribution platforms, misattributing organic installs and wasting ad spend.

These clicks can be triggered by:

Hidden scripts running in mobile webviews
Third-party software development kits (SDKs) included in apps
Background processes that respond to system events, like unlocking the device or launching an app

Common tactics used in click spam include:

Creating invisible clicks without displaying ads
Simulating engagement while the user interacts with unrelated content
Generating continuous clicks from background apps (such as launchers or utility tools)
Misreporting impressions as clicks
Spoofing or recycling device IDs to make interactions appear unique

These signals are sent silently, without the user knowing. Attribution platforms record them as valid clicks. Later, if the user downloads an app, the most recent recorded click, even if it was fake, may be credited. Fraudsters often send a high volume of these clicks over time, a method known as click flooding, to increase the likelihood that one of them will result in a real install.

As a result, downloads that should be classified as organic are attributed to paid sources. Advertisers end up paying for users they didn’t actually acquire, and campaign data becomes less reliable.

Real-world examples of click spam in mobile apps

Click spam has been identified across various app categories where background activity, in-app browsing, or frequent user interactions create openings that fraudsters can take advantage of. In these environments, fraudulent components such as unauthorized SDKs or embedded scripts have been used to send clicks, often without the knowledge of the app developer.

In the utility category, apps like launchers and memory tools run persistently and often respond to system events. In known cases, this behavior has been used to generate clicks in response to common actions, such as unlocking the device.

Gaming apps with long user sessions and frequent in-app activity have also been exploited, particularly in unauthorized versions or those with bundled third-party SDKs. In some cases, clicks have been initiated during gameplay or while idle.

Travel and aggregator apps sometimes rely on in-app webviews to load third-party content. Some of these implementations have been found to submit click attempts when users browse listings, even when no ad was visible.

Why click spam matters for mobile marketers

Click spam introduces several risks that affect attribution, budget efficiency, and strategic decision-making.

Impact on attribution accuracy

Click spam manipulates last-click attribution by placing fake clicks ahead of real installs. This causes credit to be assigned to the wrong source, making some channels appear more effective than they are, leading to inaccurate reporting.

Marketing budget waste

When fake clicks result in credited installs, marketers pay for users they did not actually acquire. This results in marketing budget waste, especially when click spam scales across networks or campaigns. Even minor attribution errors can affect return on ad spend (ROAS).

Organic traffic hijacking

Click spam often intercepts users who would have downloaded the app on their own. When these installs are incorrectly attributed, marketers lose visibility into their actual organic performance. This can distort benchmarks and undervalue owned or earned channels.

Impact on optimization and strategy

Inaccurate attribution can lead to poor investment decisions. Marketers may increase spend on underperforming sources or reduce spend on channels that are working. Over time, this weakens campaign optimization and acquisition strategy.

Click spam vs. other mobile ad fraud methods

Click spam is one of several tactics used to manipulate install attribution. Here’s how it compares to others:

Mobile ad fraud type	How it works	When it happens	Key traits
Click spam	Sends fake clicks without user interaction. The goal is to have a fraudulent click recorded before a real install.	Before an install, sometimes hours or days in advance	No real ad shown; relies on high volume and timing (click flooding)
Click injection	A malicious app detects an install in progress and quickly fires a fake click to get credit.	Right before the first app launch	Android-specific; exploits system broadcasts
SDK spoofing	Simulates install or engagement signals by imitating SDK communications, without a real device or user.	Entirely server-side	No app activity, no click, no device:entirely fabricated signal
Fake installs	Generated using real or emulated devices, bots, or automation to simulate legitimate installs. May involve device farms, ID resets, or traffic laundering.	Around the install event (real or simulated)	Can use real apps or devices; may be mixed with organic or paid traffic to avoid detection

How to detect and prevent click spam

The following methods can help teams identify signs of fraud and protect ad spend.

Analyze click-to-install time (CTIT)

In legitimate campaigns, installs typically occur within minutes of the click. Click spam often results in longer CTIT, since the click is fake and the install happens independently. A high volume of installs with extended CTIT, measured in hours rather than minutes, is a common indicator of click flooding and attribution fraud.

Monitor click-to-install conversion rates

Click spam sources often produce large volumes of click attempts with very few resulting downloads. These low-conversion sources can be flagged using fraud detection tools that compare click volume to conversion performance.

Use device fingerprinting

Device fingerprinting can detect recurring characteristics across installs. For example, if installs come from devices with matching IP addresses, reset device IDs, or identical configurations, it may suggest coordinated click spam activity.

Identify suspicious traffic patterns

Look for actions such as sudden traffic spikes, rapidly rotating sub-publisher IDs, or installs from sources that consistently deviate from normal performance ranges. These patterns often indicate invalid activity.

Click spam and Adjust

Adjust’s Fraud Prevention Suite proactively detects and blocks click spam before it reaches attribution data. It uses real-time rules, behavioral modeling, and device-level signals to identify suspicious traffic and prevent ad fraud.

Through distribution modeling, Adjust analyzes click trends across large datasets to identify anomalies associated with click flooding and other non-human behavior, thereby protecting ad spend and enhancing attribution accuracy.

All rejected clicks and installs are logged, along with rejection reason callbacks, providing networks and partners with visibility into blocked activity. This enables marketers to audit outcomes and base decisions on verified data.

As part of a broader fraud prevention stack, Adjust’s solutions support accurate attribution, campaign reporting, and strategic optimization.

Ready to see how Adjust can help you prevent ad fraud and protect your data? Request a demo today.

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