Cybersecurity|Cybersecurity statistics
Call of Duty tops cheat-related searches among online games
While online gaming draws hundreds of millions of players into virtual worlds every day, the fair play that keeps these gaming communities together is always under threat. The excitement of a well-played match is often spoiled by players who trade honesty for an unfair advantage, making the experience unpleasant for others. As the stakes rise, from casual ranked games to major esports tournaments, the urge to cheat remains strong.
To combat this, developers have introduced more advanced measures, such as behavior analytics, hardware bans, and strict and privacy-invasive anti-cheat systems. However, the situation is changing quickly. Cheaters are using new tactics and AI to automate precision, imitate human behavior, and avoid traditional detection methods.
The analysis, which looked at search interest for cheating terms across 15 popular online multiplayer games, provides insight into why players might seek unfair advantages, how effective anti-cheat technologies are, and the serious cybersecurity threats present in the online gaming community.
Key insights
- Video game players search for cheats to find ways to bypass progression walls or gain unfair advantages in gaming environments. Analysis of the global monthly search volume for cheating keywords (such as aimbot, wallhack, cheat, and hack) for 15 popular online multiplayer video games shows that Call of Duty is the title that attracts the most cheating interest with 66 searches per 1,000 players. This is followed by the closest competitor, Rocket League (59 per 1,000), while Rainbow Six: Siege comes in third place with 53 searches per 1,000 players.
- Multiplayer Online Battle Arena (MOBA) games demonstrate the highest level of community integrity among gaming genres, averaging only 0.3 searches per 1,000 players for cheating-related content. This stands in contrast to Action games (40 per 1,000), Battle Royale games (28 per 1,000), and Shooter games (23 per 1,000). The intricate decision-making processes inherent in MOBAs may render traditional "hacks" technically impractical, suggesting that game architecture and genre-specific mechanics effectively deter cheating intent.
- Data suggests that Kernel-level anti-cheating (a software that runs at the core of a computer’s operating system to detect if a player is cheating) could be a deterrent against community curiosity. Games employing Kernel-level anti-cheat maintain a lower search interest, averaging 20 searches per 1,000 players, compared with games that employ User-level anti-cheating software (namely Rocket League, Marvel Rivals, and Dota 2), with 35 searches per 1,000 players on average.
- Unlike User-level anti-cheating software, which is restricted to the same limited permissions as a standard application, kernel-level drivers operate at the heart of the operating system with total visibility into hardware and system memory. This privileged access allows the anti-cheat to detect "hidden" processes and driver-level hacks that user-level systems simply cannot see. According to data from Levvvel¹, 338 video games already use kernel-level anti-cheat software, with the most popular software being Easy Anti-Cheat by Epic Games, found in 155 popular titles such as Fortnite and Dead by Daylight (both averaging 20 monthly cheat-related searches). The increased difficulty of bypassing harder anti-cheating measures, combined with the risk of permanent hardware-ID bans, creates a barrier that could discourage non-technical players from even initiating searches for cheats.
- Despite being famously "hard to cheat" due to its server-authoritative physics, Rocket League ranks second overall with 59 searches per 1,000 players. This high volume highlights a shift in cheater behavior: when traditional "speed" or "ball" hacks are technically impossible, interest migrates toward sophisticated AI-driven bots and prediction scripts². The popularity of these searches suggests that even in "unhackable" environments, player desire for automated mastery could be a driver of search activity. Unlike traditional cheats, these AI tools use algorithms for object detection, such as YOLO³, to analyze game visuals and calculate, for example, the perfect shots to target enemies, then mimic human input (mouse/keyboard)⁴. Because these tools can run on a secondary computer or hardware device (like a capture card) and simply emulate mouse movements, they could be invisible to traditional anti-cheat software. This transition to hardware-based, visual AI cheats could make "unbreakable" security increasingly difficult to maintain.
- From a cybersecurity perspective, the high search volume for cheats in games like Call of Duty represents an attack surface for threat actors. Because "cheats" inherently require users to disable antivirus software and grant high-level permissions (especially for Kernel-level games), the high search volume could act as a funnel for distributing malware, such as info-stealers and remote access trojans (RATs). This data suggests that the "cheating interest" in these communities doubles as a serious personal cybersecurity risk for the millions of players seeking an unfair advantage.
Methodology and sources
A list of 15 online PC video games with some form of competitive multiplayer was created based on global popularity and active player base rankings, primarily utilizing Steam charts and other industry market data.
Global search volume data was retrieved in February 2026 using the Ahrefs search analytics platform, and it refers to global search volumes based on the average monthly volume over the most recent 12-month period.
To isolate interest specifically related to cheating, the search data was restricted to four primary high-intent keyword modifiers. For each title, the dataset includes the combined volume of: [Game Name] + hack, [Game Name] + cheat, [Game Name] + wallhack, [Game Name] + aimbot. Where the full game name retrieved no results, we used a common acronym for the game in question.
To maintain temporal consistency with the keyword search data, average monthly players were calculated using up to 12 months of historical data where available. For more recent releases, the maximum available window of historical data was utilized to ensure the most stable representative average. Data was retrieved in February 2026.
A cheat interest ratio was then calculated by dividing the total monthly search volume by the estimated monthly players. This measures the search pressure within a community of players. To make the data more interpretable for benchmarking and comparative analysis, the raw ratio was scaled to a "per 1,000" basis by multiplying it by 1,000. This shifts the unit of measure from a single-player probability to a community-level prevalence metric.
Following the primary calculation, games were further classified by:
- Game type: 4 groups based on gameplay type, namely Action, Battle Royal, Multiplayer Online Battle Arena (MOBA), and Shooter to identify genre-specific cheating incentives.
- Anti-cheat architecture: categorized by defense level (Kernel-level vs. User-level) to observe the relationship between defensive intensity and community search intent. Data was collected by cross-referencing a Levvvel database and information available on Steam.
