Advanced Cheat Development and Anti Cheat Bypass Techniques in Video Games

Introduction

In the perpetual cat and mouse game between cheat developers and anticheat engineers, both sides have escalated their tactics to unprecedented levels of technical sophistication. Modern video game cheats are no longer simple memory hacks or client side scripts; they leverage everything from kernel drivers and custom hypervisors to direct memory access hardware and even machine learning. In response, anticheat systems have evolved into complex multi component defenses, some operating at the kernel level with boot time drivers, designed to detect and thwart these advanced intrusions. This in depth article explores the state of the art in both cheat development and anticheat mechanisms. We will examine how current leading anticheat platforms like Riot’s Vanguard, Activision’s Ricochet, and Roblox’s Byfron/Hyperion work under the hood, including their architecture, kernel level enforcement, integrity checks, memory protections, and detection techniques. We’ll then delve into cutting edge cheat bypass strategies: from DMA attacks and IOMMU (VT-d) bypasses to System Management Mode (SMM) exploits, DKOM and kernel hooking tricks, hypervisor based evasion (with VM exit and nested paging manipulation), syscall trampolines and context manipulation, and hardware ID spoofing methods. We also discuss the emergence of AI powered anticheat defenses that model player behavior to catch subtle cheating patterns. Finally, we review the historical evolution of anticheat systems and look ahead to future directions and improvements on the horizon. The tone throughout is educational and technical, aimed at understanding these mechanisms, NOT at encouraging unethical behavior.

(With that clear disclaimer in mind, let’s dive into the technical depths of modern cheat development and anticheat bypass techniques.)

A Brief History of Anti Cheat Evolution

To appreciate the current landscape, it’s worth reviewing how anticheat systems have evolved. In the early 2000s, as online multiplayer gaming took off, developers introduced the first client side anticheat tools to combat an explosion of rudimentary hacks. PunkBuster, introduced around 2000, was one of the pioneers. Developed by Even Balance, PunkBuster worked by scanning the local system’s memory contents for known cheat signatures. It ran alongside games like Return to Castle Wolfenstein and later the Battlefield series, looking for unauthorized modifications. PunkBuster could automatically kick or ban players if cheats were detected, and it also allowed server admins to enforce manual bans. Notably, PunkBuster introduced features like periodic screenshot capture of a player’s game to detect visual hacks (though cheat developers soon learned to blank out or “clean” these screenshots to evade detection). Despite its innovative approach at the time, PunkBuster was limited to user mode scanning and was often a step behind clever cheat developers who found ways to hide from its signature based detection.

Around the same time, Blizzard’s Warden module (mid-2000s) took a similar approach for games like World of Warcraft. Warden would periodically scan the user’s running processes and memory to identify known cheat programs or unauthorized modifications. This included checking window titles and memory patterns of other programs while the game was running. Warden’s aggressive scanning raised privacy concerns though, it was labeled “spyware” by some privacy advocates, and even became the subject of legal action and debate. Nonetheless, it was an early example of an anticheat that combined memory signature scanning with some heuristic detection of unusual software behavior.

As cheating techniques grew more advanced, newer anti cheat solutions emerged in the 2010s with stronger measures. Valve’s Anti Cheat (VAC), integrated into Steam, initially stuck to user mode strategies like signature scanning and heuristic analysis of player statistics. VAC became known for its “delayed ban” strategy, detected cheaters wouldn’t be banned immediately but rather in periodic waves, to obscure which cheat triggered the detection. This cat and mouse dynamic forced cheat developers to constantly verify if their latest builds were detectable.

Third party solutions like BattlEye and Easy Anti Cheat (EAC) gained prominence by adopting a more aggressive stance. These systems began as user mode services but gradually incorporated kernel level drivers for deeper access to the system. By running in the Windows kernel (Ring 0), BattlEye and EAC could monitor memory and processes with higher privileges, making it harder for cheats to hide. They could intercept system calls and prevent unauthorized process access or memory injection that a user mode anti cheat might miss. For example, by the mid 2010s, BattlEye was routinely used in games like ARMA and PlayerUnknown’s Battlegrounds (PUBG), and it operated a kernel driver to block low level cheat behaviors (like blocking access to the game process from external tools, or scanning for known cheat drivers). These anti cheats also employed heartbeat mechanisms and integrity checks to detect if they were being tampered with.

In the competitive esports scene, specialized anti cheats like ESEA Client and FACEIT Anti Cheat pushed things even further. The FACEIT anti cheat client (popular in professional Counter-Strike: Global Offensive matches) introduced requirements such as Secure Boot and TPM 2.0 being enabled on Windows, and it even forced the removal of known vulnerable drivers before allowing play. This was done to close common avenues where cheat developers would use third party driver exploits to gain kernel access. By the late 2010s, it became clear that effective anti cheat must operate with kernel privileges or equivalent, since cheat creators were increasingly willing to run their own kernel mode code (or even entire VMs/hypervisors) to evade detection. The stage was set for the next generation of anti cheat systems, which deeply integrate with the OS.

Modern Anti Cheat Systems and Their Architectures

Today’s state of the art anti cheat solutions are exemplified by systems like Riot’s Vanguard, Activision’s Ricochet, and Roblox’s Byfron/Hyperion. Each takes a slightly different approach, but they share a common philosophy: deep integration with the operating system (often at the kernel level) and a multi faceted approach to detection. Let’s examine each of these systems, focusing on their architecture, kernel level mechanisms, and protective techniques such as integrity checks, memory monitoring, driver enforcement, and user mode analytics.

Riot Vanguard (Valorant’s Kernel Anti Cheat)

When Riot Games launched Valorant in 2020, it also debuted Vanguard, a controversial but highly effective anti cheat system. Vanguard consists of two components: a user mode client and a kernel mode driver. The kernel driver (usually named vgk.sys on Windows) loads at system boot, even before any game is launched. This always on presence is a deliberate design choice: by initializing at boot time, Vanguard can be active before any cheat program might try to hide itself, and it can also inspect every driver that loads into the OS thereafter. Competing anti cheats that load only when a game starts cannot perform this early inspection, making Vanguard’s approach unique at its introduction.

Operating in Ring 0 gives Vanguard wide ranging powers. It uses this privileged access to monitor system calls, memory, and hardware interactions at a very granular level. In fact, Vanguard sets up a series of hooks in the Windows kernel to get notified of key events. For example, research has shown that Vanguard hooks into the context switching mechanism of the OS by modifying a function in the HAL (Hardware Abstraction Layer) dispatch table. By doing so, Vanguard can intercept context switches and potentially inspect or protect the memory of the Valorant process during those critical moments. It also employs what Riot refers to as “Guarded Regions”, essentially marking certain sensitive memory regions of the game as off limits to anything except the game or the anti cheat itself. If an external process (like a cheat trying to read game memory) attempts to access these guarded pages, it triggers a page fault in the system. The result is that the cheat’s memory read is thwarted (and likely logged for detection), as the guarded memory behaves as if it’s simply not there for unauthorized access. This page table manipulation trick allows Vanguard to hide crucial game data (like player positions or object lists) behind a second layer of translation. Only code that knows how to temporarily disable or bypass the guard (which the game and Vanguard’s driver can do) will get the real data, while anything else crashes or gets bogus results. In essence, Vanguard is using virtualization-like memory tricks entirely from kernel land to protect Valorant’s memory.

Another core feature of Vanguard is its use of integrity checks and enforcement of a secure environment. The anti cheat driver continuously verifies that the game’s code and critical system libraries have not been modified in memory (detecting code injection or function detouring). If any tampering is detected – say, a few bytes of a DirectX API or a Valorant function don’t match the expected signature Vanguard can block the game or trigger a ban. Vanguard also checks for the presence of known cheat drivers or debuggers. It leverages driver signature enforcement: by default, Windows won’t load unsigned drivers without special boot configuration, but cheat developers historically bypassed this by exploiting vulnerable signed drivers. Vanguard counters this by maintaining a blacklist of such drivers and preventing the game from running if a known vulnerable driver is loaded in the system. It even requires a reboot if certain conditions aren’t met, thereby preventing cheats from simply unloading something on the fly. Riot has also encouraged security researchers to probe Vanguard through a bug bounty program, hoping to catch vulnerabilities in the anti cheat itself (since a compromised anti cheat driver would be a nightmare scenario given its privileges).

From a user mode perspective, Vanguard’s client component works in tandem with the kernel. The user mode part of Vanguard interfaces with the game process and collects higher level signals: it scans for known cheat signatures in user memory, monitors the game’s state for impossible values or events, and communicates with Riot’s servers to report suspicious behaviors. This combination of kernel mode observation and user mode analysis allows Vanguard to use both signature based detection (e.g., known cheat code patterns or handle names) and heuristic/behavioral detection (e.g., a player snapping to heads too quickly, or the presence of rogue threads in the game). All of this happens in real time, with the kernel driver acting as the vigilant gatekeeper and the user mode component as the analyst. The effectiveness of Vanguard is evident in Valorant’s notably low rate of cheating compared to some other titles, but its “always on” kernel nature also sparked debate about privacy and trust. Riot addressed some concerns by allowing players to turn off Vanguard when not playing (with a reboot required to play again) and by being transparent about why the kernel driver is needed. In summary, Vanguard represents the cutting edge of anti cheat: a deeply integrated, kernel first solution that uses hooks, guarded memory, integrity enforcement, and hybrid detection techniques to make life extremely difficult for cheat developers.

Activision Ricochet (Call of Duty’s Anti Cheat)

The Ricochet anti cheat system was introduced by Activision for the Call of Duty franchise (notably with Call of Duty: Warzone and Modern Warfare II in 2021-2022). Ricochet is also a kernel level anti cheat, but its design philosophy differs from Vanguard in a few ways. Instead of running 24/7 at system startup, Ricochet’s kernel driver activates only when the game is running, loading when you start Call of Duty and unloading when you exit. This was a conscious trade off to balance security with user trust; it limits kernel access to when it’s strictly needed, though it means there is a window (before game launch) where cheats might try to slip under the radar. Nonetheless, once active, the Ricochet driver operates with Ring 0 privileges similar to Vanguard: monitoring memory, processes, and system calls related to the game.

Ricochet’s architecture is multifaceted. Activision has emphasized that beyond the kernel driver, Ricochet uses substantial server side detection and analysis. One pillar of Ricochet’s strategy is a robust back end system that collects data from matches to identify cheaters via statistics and machine learning (more on the ML aspect later). For example, Ricochet introduced a system of gameplay replay analysis: when suspicious behavior is detected, the game client can send a recording or significant data from the match to Activision’s servers, where it can be replayed and examined for evidence of cheating. Initially, these replays were reviewed manually by security staff, but newer iterations leverage an ML model to automatically evaluate the clip and flag it with a high degree of confidence. This has greatly increased the volume of cheat identifications (Activision reported processing about 1000 suspect clips a day on one PC with the ML-assisted system). In effect, Ricochet uses client side kernel monitoring to gather forensic data, and server side intelligence to make ban decisions, a powerful combination.

On the client side, the Ricochet driver performs typical kernel anti cheat duties: it watches for known cheat software interacting with the game process, blocks unauthorized memory access or code injection, and likely performs integrity checks on the Call of Duty game code to detect tampering. It can also scan for the presence of suspicious drivers or tools. For instance, if a driver with no known signature attempts to attach to the Call of Duty process, Ricochet can flag or prevent that action. Activision has been less public about the low level technical hooks of Ricochet (compared to Riot’s somewhat more open discussion of Vanguard), but it’s understood that it prevents many of the usual cheat entry points (OpenProcess calls to the game, unauthorized DLL injection, etc.) and scans memory for patterns associated with aimbots or ESP hacks.

What really sets Ricochet apart is its mitigation toolkit. Essentially, active countermeasures deployed against detected (or strongly suspected) cheaters in real time. Instead of simply kicking a player out immediately upon detection, Ricochet sometimes keeps the cheater in the game but alters their experience in absurd ways to neutralize their impact and gather further data. Internally, when the system is confident someone is cheating, it may enable one of several “mitigation” features on that player’s client while a ban is being readied. These measures, confirmed by official Call of Duty updates, include: Damage Shield, which makes legitimate players impervious to the cheater’s bullets (the cheater suddenly finds their shots doing zero damage); Cloaking, which makes all legitimate players invisible to the cheater (they can’t see anyone to shoot at); Disarm, which literally removes the cheater’s weapons so they can’t shoot; and Splat, which prevents a flagged cheater from deploying their parachute in Warzone so that they amusingly plummet to their death when dropping into the map. The most novel mitigation added in 2023 was Hallucinations: the anti cheat client inserts phantom players into the cheater’s view that do not exist for anyone else. Aimbot users will shoot at these ghostly targets, instantly giving themselves away, while from the cheater’s perspective the game becomes confusing and full of mirages. This approach flips the script, now cheat developers have to ensure their software can distinguish real players from fake ones, adding development burden on the cheaters. All the while, the flagged player is effectively removed as a threat without them immediately knowing why. These cheeky countermeasures serve a dual purpose: they degrade the cheater’s experience (often causing them to quit on their own), and they buy time for Ricochet to collect information about the cheat method in use, such as dumping the memory of the cheat program or recording its behavior, which can feed back into improving detections. After sufficient evidence is gathered, the cheater is hit with a ban, often a hardware ban that targets their entire PC, not just the account.

Ricochet indeed performs hardware identification and banning. When a confirmed cheater is flagged, the system will collect identifiers like the machine’s MAC address, motherboard serial, graphics card unique IDs, disk drive serials, etc., and associate those with the banned account. This means if the same physical computer tries to create a new account to play, the server can recognize the hardware and deny access. (We will later discuss how cheat developers counter this with hardware ID spoofing.) Logging hardware fingerprints is a now standard anti cheat practice, but Ricochet’s kernel driver is what enables it to gather those details from the system. On Windows, getting a HDD serial or BIOS UUID might require privileges that a user mode app can’t easily get, but the kernel driver can interface with system APIs or read low level system info to retrieve them. Combined with server side analysis and trickster mitigations, Ricochet’s approach illustrates that effective anti cheat is not just about detection, but also intelligence and interference. It may not be quite as invasive at all times as Vanguard, but it compensates with creativity and heavy use of out of game analysis. Activision’s reports suggest Ricochet dramatically reduced cheaters in Warzone, though like any system it’s not foolproof, cheat developers continually adapt, and more sophisticated kernel level cheats have appeared to challenge it (some notes in the community suggest that while Ricochet curbed casual cheating, determined adversaries with kernel exploits or DMA devices still pose challenges). Nonetheless, Ricochet represents the modern multi layered philosophy: use the kernel for strong client- ide enforcement, and augment it with cloud computing and data driven detection.

Roblox Byfron/Hyperion (User-Mode Anti-Tamper in Roblox)

Roblox, a massively popular game platform, took a slightly different path by integrating an anti-tamper technology from a company called Byfron (acquired by Roblox in 2022). The result, rolled out in 2023, is often referred to as Hyperion. Unlike Vanguard and Ricochet, Hyperion currently operates solely in user mode – it is not (as of 2023) a kernel driver. Instead, Hyperion functions more like a sophisticated anti-tamper/anti-exploit shield built into the Roblox client. It’s an interesting case where the anti cheat focuses on hardening the game process against injection and manipulation, rather than rooting itself in the OS kernel.

Hyperion was introduced with the new 64-bit Roblox client in May 2023. One immediate effect was that 32-bit clients were deprecated, because the Hyperion protection only works on 64-bit Roblox. This move closed off some legacy avenues of attack that exploiters had been using on the 32-bit version. It also meant Roblox for Windows could no longer run under Wine on Linux for a time, because the anti-tamper wasn’t compatible, essentially Roblox chose security over broad compatibility. (Roblox has since been exploring ways to restore some compatibility, but the priority was clearly to get robust cheat protection in place.)

So what does Hyperion/Byfron actually do? Being an anti-tamper solution, it employs techniques similar to those used by commercial DRM or anti cheat packers: code obfuscation, integrity checking, and dynamic threat detection. The Roblox client with Hyperion is heavily obfuscated, key game code is likely encrypted in memory or packed in such a way that if an external program tries to read or modify it, it will either be unreadable or trigger a defense. Byfron’s technology reportedly uses virtualization and bytecode encryption for parts of the game’s code, meaning the Roblox processes is running segments of code in a virtual machine-like environment that is difficult for outsiders to hook or comprehend. If a cheat does manage to attach to the Roblox process and manipulate something, Hyperion is designed to notice and immediately terminate the game client (crashing it) when it detects “bad software” interactions. For example, if an exploiter injects a known DLL or calls forbidden functions to tamper with Roblox memory, Hyperion will simply kill the process as a fail safe response. This is effective in stopping many casual cheating tools which rely on open process handles or known injection methods.

Under the hood, Hyperion likely sets up trap regions or uses Windows API hooking to detect tampering. It might hook functions that are commonly used by cheats, such as LoadLibrary (to catch DLL injections) or WriteProcessMemory (to catch external memory writes) when those target the Roblox process. It also undoubtedly verifies the integrity of Roblox’s own code segments, using checksums or hashing, to ensure nothing has been patched in memory. If any unexpected modification is found, it’s game over (literally) for that session. There’s also a strong possibility that Hyperion uses just-in-time (JIT) code virtualization or mutation: meaning the actual game code running on your PC is scrambled in a way that only Hyperion’s runtime can interpret, making it extremely hard for cheat developers to figure out where to hook or what to modify. This kind of protection is analogous to how some PC game DRM works (like Denuvo Anti-Tamper), but here it’s specifically tailored to detecting Roblox exploits.

One interesting note is that Hyperion’s purely user mode approach could evolve. A Roblox developer forum post from 2023 confirmed that Hyperion was user mode only at that time but hinted “this might change” in the future. It suggests Roblox is considering a kernel component for even stronger protection (perhaps a driver to block memory access, similar to other anti-cheats). But even without a kernel driver, Hyperion has significantly raised the barrier for cheating in Roblox. Long time exploiters found that many of their tools no longer worked when Hyperion went live, the “golden era” of easy Roblox exploits came to an end. Some bypasses were found by the community (for instance, initially one could avoid Hyperion by running the 32-bit client or using the UWP app version of Roblox, which didn’t have Hyperion, but Roblox quickly moved to close those loopholes). By late 2023, Roblox had largely rolled Hyperion out platform wide.

In summary, Byfron’s Hyperion focuses on client integrity and tamper detection rather than broad system surveillance. It exemplifies an anti cheat philosophy where the game program is fortified to be a “hard target” even if a cheat can run on the same machine, it struggles to understand or modify the game’s execution. This is slightly different from Vanguard’s model of an omnipresent watchdog in the kernel, but in practice, the goals are aligned. Hyperion/Byfron shows that there is still room for user-mode anti cheat techniques especially in a controlled platform like Roblox, where the developers can mandate updates and lock down the client heavily. We may yet see a kernel mode addition to Hyperion for even more security, but already it has changed the Roblox exploiting landscape dramatically.

Advanced Cheat Development Techniques and Bypass Methods

As anti cheat systems have become more heavy handed, hooking into the OS, scanning memory, and even leveraging machine learning. Cheat developers have correspondingly escalated their methods to bypass or evade detection. In this section, we delve into some of the most advanced cheat techniques observed in the wild or conceived by security researchers. These are not the garden variety wallhacks or aimbots, but rather the deep technical tricks used to hide those cheats from modern defenses. We will explore each technique, explain how it works, provide some technical examples (code snippets in C or assembly where appropriate), and discuss how anti cheat systems might detect or prevent these tactics. The techniques include: DMA hardware attacks, SMM based exploits, DKOM (Direct Kernel Object Manipulation) and kernel function hooking, hypervisor based evasion and EPT hooking, syscall shadowing and user mode trampoline hooks, hardware ID spoofing, and more. It cannot be overstated that these methods are highly complex and often illegal to deploy against games, the discussion here is meant to shed light on the cat and mouse nature of system security in gaming.

Direct Memory Access (DMA) Attacks and IOMMU Bypasses

One of the most notorious hardware based cheat methods involves Direct Memory Access. In a DMA attack, the cheater uses an external device (often connected via PCIe or Thunderbolt) that can read and write the target PC’s memory directly, without going through the CPU or OS. This can be as simple as a specialized PCIe card (some cheat providers sell FPGA-based “DMA cards”) or even a second PC attached via a Thunderbolt cable. The appeal of DMA from a cheater’s perspective is that no software runs on the target machine, so even the best anti cheat software might not see any malicious process. The external device, acting as a bus master, can scan RAM for the game’s memory (like player coordinates, health values, etc.) and even patch memory to, say, flip a “god mode” flag, all under the nose of the OS. Traditionally, operating systems couldn’t easily defend against a rogue device doing this because DMA is by design a trusted mechanism for hardware (like GPUs or network cards) to access memory efficiently.

Modern systems introduce a defense: the IOMMU (input output memory management unit), branded by Intel as VT-d for virtualization directed I/O. An IOMMU can impose memory access restrictions on DMA capable devices, effectively sandboxing what physical addresses a device can access. In Windows, features like Kernel DMA Protection leverage the IOMMU to block external DMA from touching sensitive memory unless the device is explicitly allowed. High-end motherboards and laptops with Thunderbolt often have this to prevent “evil maid” attacks (like someone plugging a device to steal data from a locked laptop via DMA). However, not all gamers enable these protections (some BIOS require it turned on), and older machines might lack IOMMU support. Cheat developers target those weak points, and in some cases even instruct users to disable DMA protection if possible.

To illustrate how a DMA cheat might work, consider a simple scenario: a FPGA board is plugged into a PCIe slot while a game is running. The board is programmed (with firmware) to scan memory for a specific pattern or signature unique to the game’s memory layout, perhaps the sequence of bytes corresponding to the player’s coordinates in memory. Once found, the device continuously reads that memory and sends it out to a second PC. The second PC can run a radar cheat (displaying all players on a map) since it knows everyone’s coordinates by reading the game memory externally. All of this happens with zero software running on the game PC; to anti cheat, it looks like maybe an unknown PCI device is plugged in, but it’s hard to see any “program” doing the cheating. This was a real method used by some cheat providers for games like PUBG and Fortnite.

Intel VT-d and IOMMU bypass: Anti cheat vendors and platform providers haven’t been idle against DMA. They use IOMMU in two ways: (1) enabling kernel DMA protection by default (especially on laptops) so that during runtime an untrusted device can’t just start reading all of memory, and (2) scanning for known DMA devices and usage. ESEA, a competitive CS:GO platform, claimed a few years ago that they could even detect DMA hardware even if the device tried to spoof its identity. They did this by looking at subtle hardware characteristics, for example, the configuration of the device’s PCIe BAR memory regions, the number of interrupts or capabilities it had, basically a fingerprint that doesn’t rely on the device’s reported vendor ID. If something looked fishy (e.g., a device claims to be a network card but has a memory layout unlike any real one), the anti cheat can flag it. Below is a simplified snippet of pseudocode (in C) that an anti cheat driver might use to enumerate PCI devices and check for suspicious configurations:

#include <ntddk.h>
void ScanPCIDevices() {
    for (int bus = 0; bus < 256; ++bus) {
        for (int slot = 0; slot < 32; ++slot) {
            for (int func = 0; func < 8; ++func) {
                PCI_COMMON_CONFIG config;
                if (ReadPciConfig(bus, slot, func, &config)) { 
                    // Suppose we have the config space of the device
                    if (config.VendorID != 0xFFFF) { // device exists
                        // Check BAR sizes and types
                        for (int bar = 0; bar < 6; ++bar) {
                            uint32_t barVal = config.BaseAddresses[bar];
                            // Analyze barVal for unusual memory size or type
                        }
                        if (IsSuspectDevice(config)) {
                            ReportCheat("Suspect DMA device detected", config);
                        }
                    }
                }
            }
        }
    }
}

In reality, the anti cheat might use Windows APIs (e.g., IoGetDeviceProperty or querying the ACPI table for devices) rather than manually scanning PCI like above, but the concept stands: enumerating hardware to find devices that match known cheat hardware profiles. There are also heuristics like timing tests: Some anti cheat clients send large dummy buffers to the GPU or other devices and measure how memory is accessed, looking for anomalies that could indicate a snooping device.

From the cheat side, bypassing IOMMU is tough, if it’s properly enabled, the device simply cannot read protected memory. Some attackers resort to disabling IOMMU in BIOS (which anti cheat like FACEIT will notice and complain about). Others found that certain motherboards had IOMMU enabled for Thunderbolt ports but not internal PCIe slots, so by installing the DMA card internally they could still get full access. A more brazen approach: cheat developers created kernel drivers whose sole job was to disable or misconfigure the IOMMU at runtime (if they already have kernel access via a vulnerable driver exploit). For instance, a cheat driver might clear the DMA protection policy in the registry or even directly program the VT-d registers to allow all access (this is low level sabotage, likely to crash the system if done wrong). In fact, a tool dubbed "DieDMAProtection" was shared in cheat circles to disable Windows 10’s kernel DMA protection via a driver. This again shows the lengths of the cat and mouse game, anti cheat says “enable DMA protection”; cheat devs say “fine, we’ll turn it off ourselves if we can get in the kernel.”

Overall, DMA attacks represent a kind of “out of band” cheating that is hard to counter without platform support. Consoles don’t really face this (closed hardware), but on PC it remains a threat. The best mitigations combine IOMMU enforcement and hardware fingerprinting. Going forward, we may see anti cheat requiring IOMMU on for all players (some already do), and perhaps even collaboration with hardware vendors to mark or block known cheat devices. But as long as general purpose external interfaces exist, the possibility remains for a well resourced cheater to spy on game memory beyond the reach of software countermeasures.

SMM-Based Cheats (System Management Mode) and Real-World Feasibility

If kernel mode cheats and DMA cheats are not extreme enough, there’s an even more privileged realm that has been theorized for use in cheating: the CPU’s System Management Mode (SMM). SMM is an ultra privileged mode on x86 processors (often called “Ring -2” or “Ring -3” if kernel is Ring 0, to indicate it’s even deeper than the OS). It is intended for firmware level code (like BIOS/UEFI routines) to handle low level system management interrupts (SMIs) transparently to the OS. Code running in SMM executes in a special memory region (SMRAM) that the OS cannot access or even see, and it can read/write all of memory while being totally invisible to the operating system. In essence, SMM is like a mini operating system that lives below the hypervisor/OS, used for things like power management, OEM firmware drivers, etc. From a rootkit perspective, SMM is the holy grail, a rootkit in SMM can potentially subvert everything and be nearly undetectable by conventional means. Naturally, the question arose: could a cheat be implemented in SMM, making it essentially invisible to anti cheat software (which lives in Ring 0 or Ring 3)?

The idea of SMM based cheats moved from speculation to proof of concept in some security conferences. Researchers have demonstrated SMM rootkits that could theoretically manipulate a running game. For example, if one could inject code into the BIOS or UEFI firmware that triggers on a timer SMI, that SMM handler could scan the memory of a game for player coordinates and modify them (just like a DMA device could) and the OS anti cheat would have no clue, it’s “ghost code” running outside their jurisdiction. SMM has access to all physical memory, so it can read or write the game’s memory at will, similar to DMA but even more stealth because it’s running on the main CPU in a mode the OS can’t monitor. Moreover, SMM code cannot be preempted by the OS – when an SMI occurs, the OS is essentially paused while the SMM code runs, and then resumes as if nothing happened.

However, turning this into a practical cheat is exceptionally difficult. The biggest hurdle is that getting code into SMM requires exploiting firmware. One would need to either flash a custom BIOS or find a vulnerability in the existing firmware to inject a payload into SMRAM. This is non trivial and usually specific to the motherboard/BIOS in question. Additionally, modern systems have mitigations: for instance, Intel’s Platform Firmware Protection (like BIOS Guard, Boot Guard) and runtime BIOS resilience features try to prevent unauthorized code execution in SMM. It’s also worth noting that anti cheat or OS defenses can include SMM detection traps e.g., timing checks can sometimes catch the lengthy pause caused by SMM interrupts if they occur too frequently, or hardware performance counters might detect unusual patterns if an SMI is doing a lot of memory access.

From what is publicly known, real world SMM cheats are rare to non existent in online games (at least as of 2025). The feasibility is mostly demonstrated in research settings. There are some SMM Cheat Frameworks popping up in the wild like Pluton, however I digress. The complexity and risk (flashing a custom BIOS can easily brick a system and is beyond what most cheaters would attempt) make it impractical for widespread cheating. That said, the concept is important because it represents a theoretical upper bound of cheat stealth. If anti cheats someday somehow locked down kernel and hypervisor spaces completely, cheat developers could investigate firmware level attacks.

To illustrate just how SMM could be leveraged, here’s a conceptual example: imagine a custom UEFI module that installs an SMI handler which runs every second. In pseud assembly (very simplified), the SMI handler might do:

; Pseudo-code/assembly for an SMM cheat (conceptual)
SmiHandler:
    push all registers
    ; Let's say we have the physical address of the game's player list (found ahead of time or via heuristic)
    mov rax, [player<i>list</i>phys_addr]   ; read player list from physical memory
    ; loop through players and modify health (godmode) as an example
    mov rcx, [rax + health_offset]     ; get health value
    mov [rax + health_offset], 9999    ; set a high health
    ; (In reality, one would have to translate guest physical if paging, etc.)
    pop all registers
    rsm    ; Resume back to normal operation

This snippet is imaginary, in reality SMM handlers are written in firmware languages and have to deal with segmented addressing, etc. But logically, the above would boost the player’s health to 9999 every second by directly poking the memory, with no process in Windows seeing this happen. The anti cheat would only see that suddenly the health value changed in memory unexpectedly, which might be caught if the anti cheat has its own integrity check on that value. However, if done carefully (e.g., the cheat only reads memory to provide an ESP hack rather than writes), it could be nearly impossible to detect via software means.

In conclusion, SMM based cheats remain more of a theoretical “Bond villain” weapon in the cheat arsenal. They demonstrate what could be done if one has full control of a system’s firmware. Anti cheat developers are aware of such possibilities, and indeed, the presence of SMM rootkits is one reason behind initiatives like Dynamic Root of Trust (DRTM) and stricter firmware validation. From a gaming standpoint, we’re not there yet, it’s a scenario mostly of interest to security researchers and extremely determined adversaries, rather than common cheat circles. But it underscores a theme: the deeper the anti cheat goes (kernel, hypervisor), the deeper a cheat might go in response (firmware, hardware), if the incentives are high enough.

DKOM: Direct Kernel Object Manipulation and Kernel Hooking Techniques (SSDT/IDT/EPT)

When cheat developers operate in kernel mode (either via a custom driver or by exploiting a vulnerable driver), a wide range of powerful techniques opens up. A classic category is Direct Kernel Object Manipulation (DKOM) essentially, altering the kernel’s own data structures to conceal the cheat or give it extra capabilities. Hand in hand with DKOM, kernel cheats often employ function hooking at the kernel level, such as SSDT hooking (System Service Dispatch Table modification), IDT hooking (Interrupt Descriptor Table), or even manipulating page tables via EPT (Extended Page Tables) in hypervisor context. We’ll cover these in turn, as they are fundamental tricks to subvert the operating system and dodge anti cheat monitoring.

Direct Kernel Object Manipulation (DKOM): The Windows kernel maintains various linked lists and structures to keep track of processes, threads, loaded modules, etc. In a DKOM attack, a malicious driver can directly modify these structures to hide something. For example, every process in Windows is represented by an EPROCESS structure, and all active processes are linked in a doubly linked list. A cheat driver could remove its own process (or the game’s process, if trying to hide the fact it’s running) from this list by patching the pointers, effectively making the process invisible to normal enumeration. Similarly, a cheat might hide a malicious driver by unlinking its DRIVER_OBJECT from the kernel’s list of loaded drivers, or hide an open handle by manipulating handle tables. This is a tactic borrowed from rootkits: for instance, the infamous TDL3 rootkit in the past would hide processes and files by DKOM. In the context of games, a cheat might hide the presence of an auxiliary cheat process so that even if an anti cheat looks at running processes, it doesn’t see the cheat. Another use is to fake or elevate privileges e.g., modify an EPROCESS token to gain SYSTEM rights, or alter a flag in a kernel object to disable security checks.

Anti cheats, especially kernel level ones, can and do perform integrity checks to detect DKOM. If a process ID shows up in one list but not another expected location, or if an object’s reference count is inconsistent, those can be giveaways. For example, if a thread is running in the system but its owning process is not in the active list, something’s fishy. Modern anti cheat drivers may iterate through kernel lists and ensure consistency, or use Windows APIs that are less susceptible to hiding. Still, a well implemented DKOM can be stealthy if it covers its tracks thoroughly.

Below is a code snippet in C illustrating a simple DKOM process hiding technique on Windows (for educational purposes). This would be part of a kernel driver that has already located the target process’s EPROCESS structure:

// Simplified example of removing a process from the active list via DKOM (kernel driver code)
  VOID HideProcess(PEPROCESS pEprocess) {
      if (!pEprocess) return;
      // Offsets for ActiveProcessLinks can vary by version; assume we have offset 
      LIST_ENTRY* plist = (LIST_ENTRY*)((BYTE*)pEprocess + OFFSET_ACTIVEPROCESSLINKS);
      LIST_ENTRY* blink = plist->Blink;
      LIST_ENTRY* flink = plist->Flink;
      // Unlink this EPROCESS from the list
      blink->Flink = flink;
      flink->Blink = blink;
      // Point the process's links to itself to avoid dangling pointers
      plist->Flink = plist;
      plist->Blink = plist;
  }

In this snippet, we take the target process’s ActiveProcessLinks (which is a LIST_ENTRY in the EPROCESS struct linking to the previous and next processes) and we remove it from the chain by rewiring the adjacent entries. After this, the system’s process list no longer includes the target. An anti cheat enumerating processes via normal means (e.g., using PsLookupProcessByProcessId or walking the list) might miss the hidden process. However, note that Windows PatchGuard (Kernel Patch Protection) can detect modifications to certain linked lists and critical structures, and will crash the system if it notices (PatchGuard is a built in defense to stop exactly these kinds of unauthorized modifications). Cheat developers might disable PatchGuard or perform DKOM in ways that slip under its radar (for instance, PatchGuard doesn’t check everything, and there are techniques to stall or divert it).

SSDT Hooking: The System Service Dispatch Table is essentially an array of function pointers (in Windows 32-bit, it’s an array of pointers to kernel service routines; in 64-bit, due to KASLR and MSR based syscalls, it’s a bit different, but conceptually similar). By hooking the SSDT, a cheat (or rootkit) can intercept system calls. For example, a cheat could hook NtOpenProcess so that if any process (like the anti cheat’s user mode component) tries to open a handle to the cheat’s process or the game’s process, the hook can lie and indicate failure or hide information. This is analogous to user mode API hooking but at the kernel level for system calls. Historically, many malware samples hooked SSDT to hide files, processes, etc. In game hacks, one might hook NtQuerySystemInformation to filter out certain entries (like remove the cheat process from the list that QuerySystemInformation returns, achieving a similar effect to the DKOM above but via hooking instead of direct data patching).

However, Windows PatchGuard also watches for SSDT modifications on 64-bit systems. So traditional SSDT hooking (which involves writing to read only MSR or memory pages in the kernel) will usually trigger a crash if not done very stealthily. There are ways, such as temporarily disabling write protect bits or using return oriented programming to avoid detection, but it’s risky. Some cheat drivers only do SSDT hooking on Windows in test mode (where PatchGuard might be off) or on 32-bit systems. Others have abandoned it in favor of hypervisor techniques (discussed shortly). To illustrate hooking, here’s a conceptual assembly snippet of patching an SSDT entry on x86 (32-bit) - note, this is low-level and for demonstration only:

; Assuming eax contains address of target function pointer in SSDT
  mov edx, [MyHookFunction]     ; address of our hook handler
  cli                           ; disable interrupts
  mov [eax], edx                ; overwrite the SSDT entry with our function
  sti                           ; enable interrupts

On x64, system calls go through the LSTAR MSR (via the syscall instruction). Hooking that is another approach one could change the MSR to point to a custom system call handler. But that’s even more protected by PatchGuard.

IDT Hooking: The Interrupt Descriptor Table is another target. For instance, hooking the IDT entry for system call interrupt on 32-bit (int 0x2E) was one old method. Or hooking other interrupts/exceptions some cheats have hooked the page fault handler or debug exception handler to manipulate control flow. For example, hooking the page fault (#PF) handler could allow a cheat to intercept when certain memory is accessed (some anti cheat might use page faults for integrity checks, and a cheat could potentially intercept that). Similarly, hooking the clock interrupt could let a cheat run periodic tasks at interrupt time (though that’s very invasive and likely noticeable). IDT hooking is less common now due to PatchGuard and the complexity, but it’s part of the arsenal.

EPT Hooking (via Hypervisors): Extended Page Tables (EPT) are a feature of hardware virtualization (Intel VT-x) that add a second level of address translation for a guest OS. However, an ingenious use of EPT is to set up a hypervisor on the same machine as the game (making the game’s OS a “guest” of sorts, even if it’s still running directly on hardware with minimal overhead) and then use EPT permissions to trap access to certain memory pages. This is often called hypervisor assisted hooking. Instead of patching the code of a game function to hook it, a cheat developer can mark the target code page as non executable in the EPT but still readable. When the game (or anti cheat) tries to execute that code, the CPU will trigger a VM Exit (exit to the hypervisor) due to an EPT execute violation. The hypervisor’s VM exit handler can then, at that exact moment, redirect execution flow: it can swap out the page to another (with the hooked instructions) or simply manipulate the guest RIP to jump to a hook handler. After the hook handler runs (maybe doing its cheat function or bypass), it can then return control to the original code by restoring the original page and resuming execution. This sounds convoluted, but it effectively means you can hook a function without ever modifying any code in the guest OS’s view! To the anti cheat inside the OS, everything looks normal, memory checks out, no suspicious jumps... yet transparently the hypervisor can reroute execution. It’s a very stealthy hooking method.

A basic pseudo-code for a hypervisor VM exit handler doing EPT hooking might look like this:

// Pseudocode for a VM-exit handler handling an EPT execute violation
  void VmxExitHandler() {
      uint64_t exitReason = vmread(VMCS_EXIT_REASON);
      if (exitReason == EXIT_REASON_EPT_VIOLATION) {
          uint64_t gpa = vmread(VMCS_GUEST_PHYSICAL_ADDRESS);
          uint64_t qual = vmread(VMCS_EXIT_QUALIFICATION);
          if ((qual & EPT_VIOLATION_EXECUTE) && gpa == TARGET_PAGE_PHYS) {
              // The guest tried to execute code on the hooked page
              // Redirect to our custom page containing hook
              vmwrite(VMCS_GUEST_PHYSICAL_ADDRESS, HOOK_PAGE_PHYS);
              return;
          }
      }
      // ... handle other exits ...
  }

In this pseudo code, TARGET_PAGE_PHYS is the physical address of the game code page we want to hook, and HOOK_PAGE_PHYS is a page we set up with our modified code. We check if the VM exit was due to an execute permission violation on that target page. If so, we change the guest’s next instruction to point to our hook page (this is highly simplified, in practice one might manipulate the EPT entries rather than VMCS directly, or adjust the guest RIP, etc.). The idea is the same: intercept execution via EPT faults. Maurice Heumann’s (Momo5502) blog described how alternating the EPT permissions between execute/read can cause a trap on execute and a trap on subsequent read, allowing a “bounce” between two versions of a page, one with original code (for data reads) and one with hook code (for execution). This way, if anti cheat tries to read the code bytes, it sees the original (because the hypervisor gives it a clean copy on read), but when the CPU goes to execute, it executes the altered code (because the hypervisor swapped in the modified copy to execute). It’s like a magic trick with memory pages.

Unsurprisingly, anti cheat developers are researching ways to detect hypervisor presence and EPT hooking. They can use timing attacks (checking how long certain instructions or memory accesses take, since a VM exit incurs a small delay), or they might look for anomalies such as the CPU’s reported features (the cpuid instruction’s hypervisor bit) or irregularities in performance counters. Nonetheless, a well crafted hypervisor cheat can hide most overt signs, especially if it is a Type-1 hypervisor running underneath the OS (with no Windows process at all). This leads into the next section on hypervisor based evasion in general.

Hypervisor-Based Evasion (Custom VMMs, VM-Exit Handlers, and Nested Paging Tricks)

As anti cheats moved into the kernel, some cheat developers escalated by going one level below the OS: implementing custom hypervisors (virtual machine monitors) to run the game and anti cheat inside a controlled environment. By using CPU virtualization extensions (Intel VT-x or AMD-V), a cheat can make the entire gaming environment a virtualized guest, giving the cheat (the hypervisor) ultimate control over what the guest OS and anti cheat can see and do. This approach is complex but has been proven effective and is actively discussed in advanced game hacking communities. Essentially, the cheat becomes the “host” in a host guest relationship, without the user even noticing since this can be done with minimal performance impact if carefully optimized.

Architecture of a cheat hypervisor: A hypervisor cheat typically loads very early (it may require booting the system with a modified bootloader or exploiting something to launch before Windows, or using a driver that leverages VT-x to establish a VM). Once loaded, it places the current OS (with the game and anti cheat) into a VM context. The hypervisor can then intercept various events: IO operations, certain MSR reads, CPUID instructions, and crucially, exceptions and VM exits on specific conditions like EPT violations as described earlier. The cheat logic can be implemented entirely in the hypervisor, staying invisible to the guest OS. For example, the hypervisor could monitor memory of the game process (via guest physical addresses) and even modify it on the fly, all while making those modifications invisible to the anti cheat.

A key element of hypervisor evasion is handling VM exit events properly. The hypervisor sets up traps for certain actions. We discussed EPT execute traps for hooking above. Another example is registering a trap for the RDMSR or WRMSR instructions or for CPUID when a certain leaf is called. Anti cheats sometimes use CPUID as a way to detect hypervisors (the hypervisor bit or timing differences). A cheat hypervisor can intercept the CPUID call and lie about it clearing the hypervisor present bit or adjusting returned values. Similarly, if an anti cheat tries to read certain model specific registers or certain debug registers to see if they’ve been altered, the hypervisor can intercept those reads and present expected values, a technique often called MSR shadowing.

Nested Paging Attacks: We touched on EPT hooking which is one aspect of nested paging (SLAT) usage. Another related idea is memory cloaking, using EPT to selectively hide or alter memory from the perspective of the guest. For instance, the hypervisor could make certain pages of the cheat program invisible (read as all zeros or random data) when the anti cheat tries to scan them, but present them normally to the cheat or game when needed. It’s like having two versions of reality: one that the anti cheat sees and one that actually is. This power comes from the fact that the hypervisor controls the second level address translation, so it can dictate what physical address is returned for any given guest physical address. A trivial example: the anti cheat might try to verify the game code by reading memory A. The hypervisor can ensure that when address A is read, it redirects to a physical page containing the original, clean code. But when the CPU actually goes to execute from A, it’s mapped to a different physical page with the cheat modifications. This nested page swapping is the core of EPT based stealth manipulation.

To illustrate the hypervisor technique, consider a scenario where the cheat wants to intercept a function call inside the game (say, a function that processes player input, maybe to implement a silent aimbot). Instead of patching the function in memory, the hypervisor marks that function’s page as non executable in EPT. The first time the game tries to execute it, a VM exit occurs. The hypervisor sees the guest’s RIP is at the start of that function. It then allocates a new page, copies the original function’s bytes to it but with a twist, maybe replaces the first few instructions with a jump to a cheat handler (which lives elsewhere in a guarded region). Then it marks this new page as executable and maps it in place of the original for the guest. Finally, it resumes the guest. The function executes, but with the hook in place. After the hook executes whatever cheat code (like auto aim adjustment), it eventually wants to return to the original function body. The hypervisor could then remap the original page and let it continue, or simply incorporate the original instructions after the jump. When the anti cheat at some later time decides to scan that function’s memory to see if it’s been tampered, the hypervisor can intercept that read and give back the original bytes (since it knows anti cheat’s read attempt vs execution). This is intricate to implement correctly but has been demonstrated in practice.

Example – VM Exit Handler Skeleton: Here’s a small C/assembly mixed snippet that demonstrates how a VM exit handler might divert execution for a hooked address (this is abstracted; real VMCS interactions are done via assembly vmread/vmwrite instructions and lots of setup):

#define HOOK_ADDR    0x7ff612340000ULL  // example guest virtual address to hook
#define HOOK_HANDLER_PHYS 0x12345000ULL // physical address of our handler code

void HandleVmExit() {
    VMExitInfo info;
    vmx_vmread_all(&info);  // (pseudocode) read exit reason and qualification
    if (info.exit_reason == EXIT_REASON_EPT_VIOLATION) {
        uint64_t guestRip = info.guest_rip;
        uint64_t physAddr = info.guest_phys_addr;
        if ((info.qual & EPT_VIOLATION_EXECUTE) && guestRip == HOOK_ADDR) {
            // Set the guest RIP to our handler's physical address
            vmx_vmwrite(VMCS_GUEST_RIP, ConvertPhysToGuestVirt(HOOK_HANDLER_PHYS));
            return;
        }
    }
    // ... handle other exits ...
}

In this snippet, HOOK_ADDR is the virtual address in the guest that we want to hook. When an EPT violation occurs (meaning the guest tried to access a page in a way disallowed by EPT settings), we check if it’s an execute violation at that address. If so, we rewrite the guest’s instruction pointer to jump to our handler (which we have placed somewhere in physical memory and perhaps mapped into the guest’s address space). ConvertPhysToGuestVirt is a placeholder for logic that finds a guest virtual address corresponding to the given physical (if identity mapped or pre mapped). In practice, one might have pre allocated a guest virtual address for the hook code. After this, when we resume the VM, it will continue execution at the hook handler as if a seamless jump happened. Our hook handler can do its work (maybe call some cheat logic, then eventually jump back to the original code). This all occurs without modifying the guest memory through normal means.

Detection of Hypervisors: Anti cheat systems are aware of hypervisor cheats and have started implementing detection. They might use Rdtsc (timestamp counter) around sensitive operations to detect the slight delay from a VM exit. They may check specific CPU behavior – for instance, certain registers or behaviors that are slightly different under virtualization. Another trick: since a hypervisor might hide from the guest CPUID (clearing the hypervisor present bit), anti cheat could try to cause a conditional VM exit and see if something abnormal happens. Some anti cheats also watch for the installation of hypervisor hooks by checking MSRs related to VT-x (like checking if VMXON has been executed or if certain control MSRs are set – though a clever hypervisor can hide those too by running below the OS). It’s an arms race; advanced anti cheats will no doubt incorporate hypervisor detection, and advanced cheats in turn will try to mimic bare metal timing as closely as possible or even interfere with the anti cheat’s timing measurements.

In summary, hypervisor based evasion is one of the most technically challenging but potent cheat techniques today. It leverages the same technologies that cloud computing and virtualization platforms use, repurposed to create an invisible sandbox where cheats can operate freely. As anti cheats get better at kernel level detection, expect hypervisor cheats to become more prevalent in high-stakes cheating (e.g., high ranked competitive play where cheaters are willing to invest heavily). And conversely, expect anti cheats to potentially adopt their own hypervisors – imagine the anti cheat running below the game’s OS, so that it can’t be tampered with by a guest. This could even the playing field by taking back control of the virtualization layer. The battle might literally move to who controls the “Ring -1” level on the system. It’s a fascinating frontier in this security war.

Syscall Shadowing, User Mode Trampolines, and Context Manipulation

Moving back up from kernel land, there are also advanced techniques in user mode that cheats employ to bypass anti cheat hooks and inspections. Anti cheat software often places hooks in user space APIs (especially if the anti cheat has a user mode component like a DLL loaded into the game). For instance, it might hook DirectX functions to see if a cheat is drawing an ESP, or hook Windows APIs like NtQueryVirtualMemory to see if someone is scanning memory. Cheat developers have counter techniques such as syscall shadowing and trampolines to evade these hooks. Additionally, manipulating thread contexts and using asynchronous procedure calls are ways to execute code in patterns that anti cheat might not anticipate.

Syscall Shadowing & Direct Syscalls: One common strategy is to bypass user mode API hooks by not calling those APIs at all. Instead, cheats can invoke system calls directly. For example, suppose an anti cheat hooks the Win32 API OpenProcess in kernel32.dll or even hooks the lower level NtOpenProcess in ntdll.dll to detect if any process is trying to open a handle to the game. A cheat can avoid all those hooks by directly executing the CPU instruction for a syscall with the proper number. On Windows x64, system calls are made via the syscall instruction, which jumps into kernel mode at a location specified by the LSTAR MSR (typically into the KiSystemCall64 in ntoskrnl). If a cheat manually loads the correct registers and issues a syscall, it completely bypasses any user mode hooks because it’s not going through the hooked function pointers or import table, it’s jumping straight to the kernel. This is sometimes called syscall trampolining or using shadow syscalls. Malware authors use this trick to evade antivirus hooks as well.

Here’s a brief assembly snippet that demonstrates performing a direct system call in user mode (say, for NtOpenProcess on x64 Windows):

; Parameters for NtOpenProcess: (PHANDLE ProcessHandle, ACCESS_MASK Access, POBJECT_ATTRIBUTES ObjAttr, PCLIENT_ID ClientId)
; Assume RCX = pointer to ProcessHandle, RDX = Access mask, R8 = ObjAttr, R9 = ClientId (x64 fastcall convention)
mov eax, 0x26    ; syscall number for NtOpenProcess (this number varies by Windows build!)
mov r10, rcx     ; per Windows x64 calling convention, RCX -> R10 for syscall
syscall          ; make the kernel transition
ret

In this code, we set EAX to the system call number of NtOpenProcess (0x26 is an example for some version of Windows; it’s not fixed across versions). We then use syscall. No call to NtOpenProcess in ntdll.dll is made, thus any hook on that function is bypassed. The cheat would get a handle to a process without the anti cheat’s user mode agent noticing (unless the anti cheat also has a kernel hook on the system call, which some do). This method requires knowing the syscall numbers and the exact prototype, which can vary, making it somewhat brittle across Windows updates, but cheat devs often hardcode or dynamically resolve these.

Shadow Stacks and trampolines: Another trick is to copy legitimate code to an alternate memory location and execute it there to evade integrity checks. For instance, if an anti cheat is verifying that certain functions haven’t been tampered (by computing a hash of the code bytes), a cheat might unhook by restoring the bytes while checks run, and use a trampoline to jump to the real location. A trampoline generally refers to a stub of code that jumps to another location. Cheats can set up trampolines in various ways. A user mode trampoline example: allocate executable memory, write a small stub that jumps to the target API’s real address beyond the hook, and then call that stub instead of the hooked function. This way, even if an anti cheat’s hook tries to intercept calls to an API, the cheat isn’t calling the API, it’s calling its own stub that goes directly around the hook.

For instance, say MessageBoxA is hooked by an anti cheat (just an arbitrary example). A cheat could do something like:

// Example of setting up a user-mode trampoline to bypass a hook
void* realMsgBox = GetProcAddress(GetModuleHandle("user32.dll"), "MessageBoxA");
// Suppose first 5 bytes of MessageBoxA are overwritten by anti-cheat jump
BYTE originalBytes[5];
// We somehow know the original bytes or can reconstruct them (maybe from a clean copy or known values)
memcpy(originalBytes, "Hē E", 5);  // just dummy original bytes for example
// Allocate memory for trampoline
BYTE* tramp = (BYTE*)VirtualAlloc(NULL, 64, MEM_COMMIT, PAGE_EXECUTE_READWRITE);
// Build trampoline: place original bytes, then a jump to rest of MessageBoxA after 5 bytes
memcpy(tramp, originalBytes, 5);
uintptr_t jumpBackAddr = (uintptr_t)realMsgBox + 5;
tramp[5] = 0xE9; // jmp
*(DWORD*)(tramp+6) = (DWORD)(jumpBackAddr - (uintptr_t)tramp - 10);
// Now tramp acts as an unhooked MessageBoxA

This is a rough sketch: basically, we reconstructed the first 5 bytes of MessageBoxA (which the anti cheat overwrote with a hook) in our trampoline buffer, then we append a jump from our trampoline back to MessageBoxA+5 (the instruction after the hook). Now by calling tramp instead of MessageBoxA, we effectively call the real function without hitting the hook. The challenge is obtaining those original bytes – sometimes one can find them in a loaded module copy or by knowing the DLL version.

Context Manipulation: Cheats also manipulate the execution context of threads to achieve their ends. A known method to run code inside a game (without creating a suspicious remote thread) is to hijack an existing thread’s context. For example, using the Windows SetThreadContext function, a cheat can suspend a game thread, set its instruction pointer (RIP) to a shellcode location (perhaps in allocated memory), resume the thread to execute the shellcode (which might perform some cheat action or DLL injection), and then restore the thread’s context back to normal as if nothing happened. This is a way to execute code inside a target process while avoiding the creation of a new thread that anti cheat might detect. Anti cheats can catch this if they monitor SetThreadContext calls or if they see irregular thread behavior, but it’s a cat and mouse of who monitors what.

Another context trick is using APC (Asynchronous Procedure Calls). A cheat can queue an APC to a thread (even a system thread or another process’s thread if permissions allow) which will execute code at a point when that thread enters an alertable state. This can be used to inject a call subtly. Some injection techniques (like “Early Bird” APC injection) rely on queuing APCs in a newly created thread before it begins, so it executes the payload immediately. Anti cheat software might not catch that if they are only watching CreateRemoteThread patterns.

In summary, user mode evasion is about finding ways to do what you want (read/write memory, call system functions) without using the obvious channels that anti cheat is intercepting. Syscall shadowing avoids user mode API hooks by going lower. Trampolines and code caves avoid inline hook detection by executing code in un monitored places or restoring bytes. Context manipulation lets cheats run code on existing threads to blend in. Anti cheat developers counter these by extending their visibility: for example, a kernel anti cheat might monitor syscalls at the kernel entry (so even direct syscalls are noticed), or verify code sections in memory periodically to catch patched bytes or unknown trampolines, or watch for suspicious thread behavior (like a thread that suddenly jumps to an area of memory that was allocated at runtime, which could indicate shellcode execution).

Ricochet Kernel Mode Bypass (Technical Deep Dive)

Lastly, I'd like to talk about a bypass method that I have found through my own research, that does not require anything more than a custom driver to bypass Call of Duty's Ricochet Anti Cheat. The bypass leverages a sophisticated kernel mode driver specifically engineered to mask and obfuscate all cheat related activities from Ricochet’s intensive scanning and detection routines. Ricochet primarily depends on user to kernel callbacks, rigorous system thread scanning, and signature based detection strategies for spotting unusual memory accesses or abnormal handle operations. The custom driver is carefully designed to intercept, redirect, and sanitize these critical detection points. The bypass begins by nullifying or redirecting crucial kernel callbacks used by Ricochet, such as PsSetCreateProcessNotifyRoutineEx and ObRegisterCallbacks. By neutralizing these callbacks, Ricochet becomes unable to reliably detect events like handle spoofing or suspicious process creation, essentially blinding it to critical cheat activities.

To further bolster stealth and evade Windows' built in PatchGuard protections, the driver implements SSDT level hooks rather than conventional inline patching. Specifically, it hooks essential memory manipulation functions such as MmCopyVirtualMemory and NtReadVirtualMemory. By utilizing SSDT indirection, the driver effectively sidesteps PatchGuard's routine integrity checks, maintaining undetected kernel level control.

Complementing the callback interception, DKOM techniques are deployed to ensure the cheat process and its associated threads are effectively invisible to Ricochet’s enumeration attempts. The driver:

• Unlinks the cheat process entirely from the kernel’s ActiveProcessLinks, removing it from routine scans.
• Conceals the cheat from the PspCidTable, preventing enumeration via standard kernel queries.
• Sanitizes critical structures like ETHREAD and EPROCESS, eliminating identifiable footprints that Ricochet scans typically detect.
• Completely zeroes out the LDR_DATA_TABLE_ENTRY structure for the cheat's loaded DLL or mapped memory sections, thus evading Ricochet's module enumeration.

To further solidify its undetectability, the driver provides Ricochet with falsified responses to memory queries. It reports artificially sanitized or entirely fake memory regions, creating the illusion that no suspicious code resides within scanned high range memory addresses.

Communication between the cheat's kernel level components and user mode logic is conducted exclusively through a secured IOCTL interface. This interface uses a unique device object intentionally omitted from registration via IoCreateDeviceSecure, rendering it invisible to standard device enumerations performed by Ricochet.

Additionally, to avoid static detection signatures, the IOCTL dispatch table is dynamically rotated each time the system boots. This ensures even memory forensic techniques and static code analysis cannot reliably pinpoint and flag the cheat's IOCTL communication.

Developing this bypass took approximately four months, highlighting the complexity and careful engineering required. It's critical to acknowledge the inherent risks of bans when developing and debugging kernel level bypasses against advanced anti cheat measures. Debugging anticheat processes, particularly those with kernel level hooks and protections, is exceptionally challenging and carries substantial detection risk, necessitating meticulous planning and execution.

(As a final note, This bypass is strictly as a proof of concept and HAS NEVER and WILL NEVER be used to develop cheats for the Call of Duty franchise. I will not share any of my work with anyone under any circumstances. Please do not contact me about this subject; such inquiries will receive no response.)

Hardware ID Spoofing (MAC, SMBIOS, EDID, and More)

Modern anti cheats, as noted earlier, often go beyond just kicking a player from a match, they issue hardware bans. This means if you’re caught cheating, not only is your account banned, but the ban is tied to identifiers of your PC. The next time you try to play (even with a new account), the system sees a matching fingerprint and refuses to let you in. These hardware identifiers commonly include: MAC addresses of network cards, hard drive serial numbers, Motherboard UUID or serial (from SMBIOS), GPU identifiers, and even things like monitor EDID (the unique ID of your display). To a legitimate user, these are just fixed properties of their hardware, but to a determined cheater, they are challenge parameters – how to spoof or change these IDs to appear as a different machine and evade a hardware ban.

MAC Address Spoofing: MAC (Media Access Control) addresses are often used in bans because they’re easy to fetch and generally unique. Many NICs (Network Interface Cards) allow the MAC to be changed via software or driver settings (for instance, in Windows Device Manager or via registry). Cheats can exploit this by simply setting a new MAC address (there are tools and even simple registry scripts to do this). If not, some resort to buying a new network card (MAC addresses are tied to hardware typically). Anti cheat might use multiple MACs if you have multiple interfaces to reduce spoofing (e.g., check wired, wireless, etc.). From a code standpoint, setting a MAC on Windows might involve using DeviceIoControl on the network adapter driver or WMI calls. On Linux, it’s as simple as an ifconfig hw ether command. Many cheat “spoofers” automate MAC changes on the fly.

SMBIOS and Motherboard IDs: The SMBIOS table (accessible via ACPI or via Windows WMI calls) contains information like System Manufacturer, Product Name, Serial Number, etc. Some games record a “GUID” that is derived from these values or use them directly. Changing these is not straightforward via software, they’re programmed into the BIOS by the manufacturer. Some motherboard manufacturers provide tools to update the DMI data (e.g., for OEMs), but it’s not common. Cheaters have two main ways: reflash a modified BIOS that has altered serials (risky and not possible on all boards), or intercept the calls that fetch this info and feed fake data. For instance, if the anti cheat uses GetSystemFirmwareTable or WMI to read the BIOS serial, a cheat driver could hook that function or patch the data in memory after ACPI tables are read. One could locate the SMBIOS table in physical memory (it’s often at a known address range or can be found by signature) and modify the strings at runtime. However, some anti cheats might detect if the SMBIOS table checksum doesn’t match after modification, etc. Another approach is using a hypervisor to intercept the instruction (e.g., in instructions that read CMOS or specific ports) that retrieves these values.

Disk and GPU IDs: Disk drives have serial numbers that anti cheat may log (for example, a physical HDD/SSD serial). Some cheat spoofers will send ATA commands to the drive to temporarily report a different serial (if the drive’s firmware allows it, most don’t, as it’s read only outside factory). More commonly, they install a filter driver in the storage stack that intercepts the query for serial and substitutes a fake one. For GPUs, there’s usually a device ID and sometimes a UUID (NVIDIA GPUs have a unique identifier accessible via their driver or NVAPI). Changing those usually means flashing the GPU BIOS (rare and card specific) or intercepting queries.

Monitor EDID: EDID is the data structure that a monitor provides to describe its capabilities (including a serial number for the monitor). It seems surprising, but some anti cheats have reportedly banned monitors in addition to the PC, perhaps to catch cases where an internet cafe or shared environment is used by cheaters (if the same monitor shows up on new accounts, they know it’s likely the same person). Spoofing EDID can be done by reprogramming the monitor’s firmware (not common) or by software override (Windows allows overriding EDID via registry for custom resolutions, cheats could use that to provide a different EDID to the system). Also, a hypervisor approach: intercept the I2C communication that fetches EDID from the monitor and feed different bytes.

Example Spoofing via Driver: Below is a conceptual snippet in C (kernel driver context) that could intercept a system call fetching hardware info. Let’s say the anti cheat calls ZwQuerySystemInformation(SystemHardwareProfileInformation, ...) which returns a hardware GUID. A cheat could hook this (similar to SSDT hooking or using a filter driver) and replace the GUID:

NTSTATUS (*OriginalZwQuerySystemInformation)(ULONG, PVOID, ULONG, PULONG);

NTSTATUS HookedZwQuerySystemInformation(ULONG InfoClass, PVOID Buffer, ULONG Length, PULONG ReturnLength) {
    NTSTATUS status = OriginalZwQuerySystemInformation(InfoClass, Buffer, Length, ReturnLength);
    if (NT_SUCCESS(status) && InfoClass == SystemHardwareProfileInformation) {
        // The buffer is a SYSTEM_HARDWARE_PROFILE_INFORMATION struct
        SYSTEM_HARDWARE_PROFILE_INFORMATION* hwInfo = (SYSTEM_HARDWARE_PROFILE_INFORMATION*)Buffer;
        GUID fakeGuid = {/* some constant or random GUID */};
        hwInfo->HardwareProfileGuid = fakeGuid;
        // Also can change hwInfo->HwProfileName
    }
    return status;
}

This assumes we managed to install a hook on ZwQuerySystemInformation (which as discussed is not trivial on 64-bit due to PatchGuard, but perhaps via a hypervisor or by locating and patching the function in memory if anti cheat isn’t looking). The idea is to catch when the anti cheat tries to retrieve a hardware GUID and give it a fake one.

Many cheat providers sell spoofer tools separately from the cheat, which users run after being banned to clear or change these hardware fingerprints. They often bundle changes: e.g., change MAC, volume serial numbers (you can change Windows volume GUID by format or using vol command to set a new ID), and sometimes even throw in random BIOS SMBIOS info (which might simply blank out the DMI data by flipping some bits in memory during boot, though that can cause other issues).

Anti cheats counter spoofers by collecting multiple identifiers and using those that are hardest to change. For instance, they may hash a combination of things, CPU features, TPM ID, MAC, drive serial, etc., so even if a couple are spoofed, one might remain real. Some have moved to use the TPM (Trusted Platform Module) ID or cryptographic binding, which is much harder to fake without an actual new TPM. Microsoft’s kernel security features and platform attestation (used in features like Windows Hello or Azure Attestation) could theoretically assist anti cheat in identifying a machine uniquely in a tamper resistant way. As of now, most anti cheat still rely on the easier to get identifiers.

One should note, hardware spoofing is not a gameplay advantage per se, it’s purely to evade bans. It’s an important part of the cheat ecosystem because it allows repeat offenders to return after being caught. From an ethical standpoint and a practical one, anti cheat companies have started suing and going after the sellers of such spoofer tools as well, since they facilitate ban evasion which is often a violation of anti circumvention laws.

AI Powered Anti Cheat Defenses (Modeling Behavioral Signatures)

While cheat developers are exploiting the lowest levels of hardware and software, anti cheat teams are also innovating by looking at patterns that are much harder to hide: player behavior. The use of machine learning (AI) in anti cheat has grown recently, as games produce massive amounts of data that can be used to train models distinguishing normal play from cheating. Unlike signature scanning or memory checks, which look for the cheat program itself, these ML driven methods observe what the player does in the game. This creates a kind of “behavioral signature”, for example, how fast and accurately a player moves their aim, reaction times, smoothness of cursor movement, decision making patterns, etc. If a cheat is controlling the player (like an aimbot or triggerbot), these patterns can become discernibly non human.

One high profile example is Activision’s Ricochet, which integrated a machine learning system to analyze gameplay clips for suspect behavior. The ML model was trained on confirmed cheat incidents and normal gameplay, learning to identify telltale signs in the data that a human reviewer might miss or be too slow to catch. For instance, an AI might detect that a player’s crosshair snaps to targets in less than, say, 50 milliseconds consistently (faster than a human reflex would allow), or that their cursor movement has a perfect straight line path to the target’s head (indicative of algorithmic aiming rather than the hand’s natural slight shaking). Valve reportedly worked on a project called “VACnet” for CS:GO, which was a deep learning system specifically to catch spinbots and aimbots by analyzing thousands of matches worth of player behavior data. By feeding the model parameters like kill/death patterns, headshot percentages, movement traces, etc., it could flag likely cheaters for review.

The advantage of AI based detection is that it can potentially catch cheats that don’t leave a traditional footprint. For example, the recent trend of “audio visual” cheats, using AI to read the screen (computer vision) and control input, might leave no memory signature for an anti cheat to detect. But their behavior could still be abnormal (inhuman aim or decision speed), which an AI can spot. Similarly, a human assisted cheat (like using a hardware aimbot that helps the player aim faster but not perfectly) might still produce subtle anomalies in play style.

From a technical standpoint, these anti cheat ML models typically run server side or on a cloud infrastructure due to the heavy computation. The game client sends data either live or in batches (like after a match). Data could include: player coordinates over time, actions taken, results (hits, misses), and even raw input timings. For example, an AI might use a recurrent neural network to analyze the sequence of the player’s aim adjustments and determine if they follow a pattern consistent with aim assist algorithms. Another might use convolutional neural networks to analyze the actual game replay (almost like a video) to detect things like “the player is tracking targets through walls”, something a wallhack user might do unknowingly.

There’s also a concept of behavioral fingerprinting: creating a unique fingerprint of how a particular user plays (when they move mouse, how they move it, keypress rhythms). If that fingerprint suddenly changes drastically, it could indicate the account was sold or a cheat that automates actions is now playing. AI can cluster player behavior and see outliers.

On the flip side, cheat developers have started thinking about avoiding behavioral detection. Some “smart” aimbots intentionally add jitter or randomness to mimic human imperfection. They may miss shots on purpose or delay input to simulate reaction time. Essentially, they try to make the cheat’s behavior look statisticaly human like to fool ML models. This becomes a whole new battleground: adversarial machine learning. A cheat could even use its own neural network to decide when to activate (like only aim assist in moments that seem plausible, and not with superhuman precision every time).

One emerging area is using AI on the client side for anti cheat, for instance, analyzing screen captures on the client for overlay indicators (some cheats draw visuals that might be detectable by image analysis). But doing that in real time on client is heavy and also could be considered invasive (analyzing a user’s screen pixels with an AI could be a privacy concern unless very targeted to the game window).

We should also note the research where AI can detect anomalies in system calls or input device patterns that might indicate macro usage or hardware hacks like a Cronus (a device that automates controller input, often used on consoles to reduce recoil or perform perfect combos). A trained model could possibly notice that a controller input is too perfectly timed to be human.

To tie it back to specifics: Ricochet’s mention of an ML model processing about 1000 clips a day automatically shows how effective it can be to augment the security team. That model likely looks at each clip (which might be a snippet of gameplay around a suspected event) and outputs a confidence score of cheating. Similarly, Riot’s Vanguard reportedly uses server side analytics (maybe not outright ML in public info, but they no doubt have data models to assess players). Smaller studios might outsource this, there are companies offering “AI anti cheat” as a service where you send telemetry and they flag cheats.

In code terms, there isn’t a direct snippet since this is mostly server logic, but conceptually:

# Pseudo-code: features extraction from gameplay data for ML model
player_data = get_player_match_data(player_id)
features = []
features.append(player_data.avg_reaction_time)
features.append(player_data.headshot_ratio)
features.append(player_data.tracking_error)  # how smoothly do they track targets
features.append(player_data.suspicious_movements)  # e.g., aiming at players through wall (boolean or count)
# ... many more features ...
prediction = ml_model.predict_proba(features)
if prediction[1] > 0.98:  # 98% confidence cheat class
    flag_player(player_id)

This pseudo code assumes an ML model that outputs a probability that the player is cheating vs not. The features could be dozens or hundreds of variables computed from the match.

AI for pattern recognition can also help identify new cheat signatures in memory by clustering, or detect groups of cheaters (like if one cheat has a distinctive behavior pattern, the model might catch all users of that cheat). But primarily, it’s used to analyze gameplay.

The future of AI in anti cheat likely involves more real time on client AI (if devices become powerful enough, maybe using some of that idle GPU to run a small model that watches inputs or graphics). And for cheat creators, maybe using reinforcement learning to create cheats that are harder to detect behaviorally.

In summary, AI powered anti cheat adds a probabilistic, data driven layer to the defenses. It doesn’t replace traditional methods but complements them. It shifts some of the focus from how the cheat works to what the cheat causes the player to do. This is valuable because even a “perfect undetectable” cheat that lives in SMM or a hypervisor will still make the player perform beyond human capabilities, which can blow their cover. The hope is that even as cheats get more technically stealthy, they can’t avoid betraying themselves through actions, and AI can catch what humans or simple heuristics would miss.

Future Directions and Improvements in Anti Cheat Systems

Considering the escalation we’ve discussed, from user mode to kernel to hypervisor to firmware battles. One might wonder, what’s next for anti cheat systems? And are there any gaps left to close? The future of anti cheat will likely involve even closer cooperation with hardware and OS vendors, more use of virtualization and secure enclaves, and smarter predictive algorithms.

Hardware-Enforced Anti Cheat: We may see anti cheat features baked into hardware platforms. For example, imagine if GPUs had a mode to prevent certain kinds of overlays or memory reads unless a security token is present. Or CPUs providing more hooks for anti cheat to use virtualization safely. Microsoft has already dipped its toes with features like Kernel DMA Protection (to stop external devices) and even the concept of Game Mode or TruePlay (an older Windows 10 feature that attempted to create a protected environment for games). TruePlay, introduced in UWP, was a sort of sandbox to run games such that any cheating would be easier to detect, it didn’t take off widely, but the concept may return in another form. Console gaming remains relatively secure because of hardware/OS integration (it’s not trivial to cheat on a console without modding it), so PC gaming might move in that direction: requiring secure boot, all drivers signed (no test modes), etc. In fact, we already see some of that: e.g., FACEIT requiring Secure Boot and no known vulnerable drivers. It’s plausible that in the future, games (especially esports titles) might flat out refuse to run if the system isn’t in a “secure gaming state”, which could mean virtualization based security on, TPM verified, and anti cheat running at a level that cheats can’t tamper with.

Hypervisor based Anti Cheat: A logical progression is anti cheat itself using a hypervisor to gain the upper hand. Instead of a kernel driver that can be potentially attacked by a cheat’s hypervisor, the anti cheat could be the one in Ring -1. For instance, an anti cheat could come as a lightweight hypervisor that boots with the system (perhaps leveraging Windows Hyper V APIs or a custom bare metal hypervisor). It could then supervise the OS and games ]from below, making it very hard for a cheat to hide because the anti cheat would always be “one level deeper”. The challenge here is deploying such a system widely, it’s complex and could conflict with other hypervisors or VMs users run. But we may see specialized use cases, like tournament PCs or dedicated competitive environments using a hardened hypervisor anti cheat.

Secure Enclaves and Isolation: Technologies like Intel SGX or ARM TrustZone provide secure enclaves where code can run isolated from the rest of the system. One idea: run critical parts of the game or the anti cheat’s verification code in an enclave, so that even if the OS is compromised by a cheat, the enclave can attest whether certain computations (like an integrity check) were valid. This could, for example, securely compute a hash of the game’s code sections and sign it such that a cheat can’t forge it without the enclave’s key. However, enclave tech has its own limitations (performance, memory) and SGX in particular is being phased out on consumer chips in favor of other TEEs (Trusted Execution Environments).

Cloud Gaming and Server side Authority: A broad stroke way to eliminate many cheats is to take critical computations away from the client. If the game runs on a server (as in cloud gaming or just more authoritative server logic), the client has less to meddle with. Cloud gaming (services like GeForce Now, etc.) essentially eliminate client side cheats because the player doesn’t have access to the game process at all, they just see a video stream. It’s not widespread for all games yet due to latency and cost, but it could grow. Alternatively, games could design to make the server authoritative over more things (so even if a cheater client tells the server “I shot that guy”, the server uses physics/logic to decide if that’s reasonable). Many games already do this to an extent; future games might incorporate more cheat-proof designs at the gameplay design level (though that’s hard for things like aiming, which ultimately relies on client input).

Better Machine Learning and Data Sharing: The use of AI will likely expand. Companies might even share data of known cheaters to collectively train models (though privacy and competition issues make that tricky). We could envision an industry wide “CheatNet” where data from multiple games helps identify serial cheaters or new cheat behaviors quickly. Also, as cheats try to evade behavioral detection, anti cheat ML will get more granular – potentially analyzing raw input device data or high frequency telemetry that’s hard to fake entirely.

Human factors and moderation: Another direction is involving the community, some games like Overwatch (by Valve, not the Blizzard game) had a system where experienced players could review replays of reported cheaters (called the Overwatch system in CS:GO). Crowdsourced moderation combined with AI could be more widely used, where an AI flags something and human reviewers confirm it, improving the model over time. This helps catch the subtler cases that automated systems aren’t 100% sure about.

Fighting the Cheat Ecosystem: We’ve focused on technical aspects, but future anti cheat improvements also include legal and economic strategies. Game companies have increasingly taken legal action against cheat developers, winning lawsuits that impose fines and force shutdown of cheat services. This can deter some of the larger operations, or at least drive up the cost of cheating. They also work with platforms (like payment processors, hosting providers) to cut off cheat distribution. While not a tech improvement, it’s part of the “future directions”, a holistic approach to anti cheat that’s not just software versus software, but also targeting the business of cheats.

User Privacy and Security Balances: It’s worth noting that as anti cheats become more invasive (kernel drivers, hypervisors, scanning files), there will be continued scrutiny on privacy and false positives. Future systems will need to be more transparent, perhaps with OS level vetting. Microsoft has a program for registering kernel anti cheat drivers so they are reviewed and co signed. We might see OS APIs specifically for anti cheat to use in a safe manner (for instance, a Windows API that allows antim cheat to get a “secure snapshot” of a process memory via some kernel service, rather than each anti cheat making its own driver with full privileges). This could reduce the risk of anti cheat drivers themselves becoming vulnerabilities (a real concern, as Vanguard and others theoretically could be exploited if not coded well).

Cat and Mouse Continues: For every anticipated anti cheat advance, cheat makers will try something new. We might see cheats messing with things like power consumption or acoustic signals (esoteric, but academia has looked at using a PC’s acoustic noise or power draw to glean information from a program far fetched for a game cheat, but it shows creativity knows no bounds). More practically, cheats might incorporate AI too, for example, using a local neural network to make their aimbot aim more human like. Or using generative models to randomize their behavior to stay under detection thresholds. The war will continue on multiple fronts: speed of detection, depth of system control, subtlety of behavior.

In conclusion, the future of anti cheat will likely be a blend of deeper integration (potentially at the hypervisor/hardware level) and smarter analysis (AI and big data). The best scenario for gamers would be anti cheat that is both extremely effective and minimally intrusive a tough combination. Perhaps collaborations between OS makers and game companies can yield solutions that are secure by design. Until then, we have an arms race: each side leveraging whatever edge they can, from kernel exploits to machine learning, to either gain an unfair advantage or to uphold a fair playing field.

Conclusion and Ethical Considerations

The landscape of cheat development and anti cheat bypasses in video games has become extraordinarily advanced. What began decades ago with simple memory pokes and pattern scans has evolved into a highly technical duel involving kernel drivers, virtualization, and artificial intelligence. We’ve explored how modern anti cheat systems like Vanguard, Ricochet, and Byfron operate, peeling back the layers of their architecture and methods, from kernel hooks and guarded memory to machine learning detection of gameplay. We’ve also shed light on the dark arts practiced by cheat developers: DMA hardware hacks, SMM rootkits, stealthy hypervisors, low level kernel manipulation, and creative spoofing of hardware identities. For every trick the cheaters invent, the anti cheat engineers devise a counter and vice versa.

It is important to emphasize the ethical dimension in this domain. While we’ve discussed the technical capabilities of cheats, using or developing these in the real world is highly unethical and against the terms of service of games. Cheating ruins the experience for everyone and in professional settings can amount to fraud. Major game companies actively pursue legal action against cheat creators, and offenders face permanent bans or worse. The knowledge of how cheats and anti cheats work is valuable for defense: it helps developers build more robust systems and helps players understand why certain anti cheat measures (even invasive ones) might be necessary. This article is meant to educate on the complexity of the challenge, not to enable cheating. As gamers and as a community, supporting fair play is crucial. The hope is that by understanding these systems, more people will contribute to improving anti cheat technology or at least appreciate the lengths taken to protect games from abuse.

In the end, anti cheat development is about leveling the playing field and preserving the integrity of games we love. It’s a never ending battle, but one where each incremental improvement in security is a win for fair competition. The next time you’re playing an online match and not encountering obvious cheaters, a lot of sophisticated work behind the scenes, possibly involving kernel level guardians, AI watchdogs, and decades of security evolution is to thank for that. And if you are tempted to cheat, remember: not only is it likely to get caught by one of the many defenses discussed, but you’re also breaking the trust and rules that make gaming fun for everyone. Let’s leave the cheats in the labs as curiosities, and keep the actual games clean and based on skill.