Traditional Game Cheating Techniques, Anti‑Cheat Countermeasures, and the Rise of AI‑Based Cheats

As an FPS player, I despise cheaters and often wonder how their hacks work and why anti‑cheat systems sometimes fail; this article answers those questions by describing traditional cheat categories and their counter‑measures.

01 Traditional Cheating Introduction

Before AI‑driven cheats, most hacks fell into three groups: memory hacks (reading/writing game memory), packet hacks (modifying network packets), and simulation hacks (key‑macros, mouse macros).

Memory Hacks

Many game values reside in RAM; attackers locate the base address of a value using tools such as CheatEngine, OllyDbg, or X64Debug, then apply offsets to modify the physical address. The address changes each run, so a stable cheat must find a reliable base address and offset, which often requires knowledge of assembly and computer architecture.

Common memory‑hack techniques include:

(1) Memory Modification : Search for a value, change it in‑game, repeat until the physical address is identified.

(2) Code Injection : Load custom code into the target process and execute it, typically via OpenProcess to obtain a handle and CreateRemoteThread to run the injected code.

(3) DLL Injection : Load a custom DLL into the target process, often preferred for more complex payloads.

Example injector steps: use AdjustTokenPrivileges to elevate privileges, then call LoadLibraryA to inject the DLL, resulting in a pop‑up or other illicit behavior.

Packet Hacks

Packet hacks intercept or forge network packets. The typical workflow is: (1) locate the game's send‑packet function (e.g., via OllyDbg breakpoints), (2) find the encryption call and reverse‑engineer it, (3) craft and send custom packets to achieve desired effects. This method can bypass many client‑side checks but requires deeper protocol knowledge.

Simulation Hacks

Macro‑based cheats simulate player input. Simple mouse/keyboard macros are easy to create, while advanced macros may use AHK with OCR to detect in‑game cues (e.g., health bars) and automate aiming. Because they do not modify memory, they can evade some detection methods.

02 Traditional Anti‑Cheat Measures

Defensive techniques include:

Code obfuscation or packing ("加壳") to make static analysis harder.

Integrating robust third‑party anti‑cheat engines (EAC, Ricochet, NEAC) that scan processes, drivers, and employ anti‑debug tricks.

Adding runtime interference (e.g., random checks) to increase cheat development cost.

03 Counter‑Anti‑Cheat Strategies

Cheat developers respond with methods such as:

Re‑packing their tools to hide signatures.

Faking legitimate digital signatures or using expired certificates.

Injecting cheats into other processes to avoid direct detection.

Leveraging kernel‑mode drivers to bypass user‑mode anti‑cheat checks.

Using DMA (Direct Memory Access) hardware to read/write game memory externally, making detection extremely difficult.

04 AI‑Based Cheating

AI is now applied in two main ways: computer‑vision aimbots and game‑play bots. Vision‑based aimbots use frameworks like YOLOv5 and OpenCV to locate enemy heads via color detection or trained models, then move the mouse via hardware devices (e.g., Kmbox). AI bots for mobile games treat the decision problem as a multi‑armed bandit and employ Monte‑Carlo reinforcement learning to recommend optimal actions, dramatically improving performance in titles such as "赛马娘".

AI cheat pipelines typically involve: Collecting a large labeled dataset. Training a model (e.g., YOLO) for real‑time inference. Mapping 2D screen predictions to 3D game coordinates. Driving mouse/keyboard input via custom drivers or hardware.

05 AI‑Driven Automated Testing

The same vision techniques can be repurposed for automated QA: detecting missing assets, verifying model outputs, or even generating test cases via reinforcement learning, turning cheat technology into a testing asset.

06 Conclusion

Cheats severely degrade player experience; while eliminating every cheat is unrealistic, developers should aim to stay ahead of cheat authors by increasing development cost, employing layered anti‑cheat systems, and leveraging AI for both detection and testing.