Ags Game Hacking

Game Hacking Techniques

Overview

This skill covers game-hacking techniques documented in the awesome-game-security collection, with emphasis on how cheats move from user mode to kernel mode, hypervisors, and DMA when defenders raise the bar. It is best used to understand the offensive side of the threat model that anti-cheat systems are designed to detect.

Treat implementations, performance numbers, stealth rankings, and detection claims as versioned threat-model examples rather than guarantees. Use research-rigor when converting them into a factual claim or defensive decision.

README Coverage

• Cheat > Debugging
• Cheat > Packet Sniffer&Filter
• Cheat > Packet Capture&Parse
• Cheat > SpeedHack
• Cheat > Injection:Windows
• Cheat > Injection:Linux
• Cheat > Injection:Android
• Cheat > Injection:IOS
• Cheat > Injection:PlayStation
• Cheat > DLL Hijack
• Cheat > Hook
• Cheat > Anti Signature Scanning
• Cheat > RPM
• Cheat > DMA
• Cheat > W2S
• Cheat > Overlay
• Cheat > Render/Draw
• Cheat > UI Interface
• Cheat > Vulnerable Driver
• Cheat > Driver Communication
• Cheat > EFI Driver
• Cheat > QEMU/KVM/PVE/VBOX
• Cheat > Wine
• Cheat > Anti Screenshot
• Cheat > Spoof Stack
• Cheat > Hide
• Cheat > Anti Forensics
• Cheat > Triggerbot & Aimbot
• Cheat > WallHack
• Cheat > HWID
• Cheat > Bypass Page Protection
• Cheat > SDK CodeGen
• Cheat > Game Engine Explorer:*
• Cheat > Explore UWP
• Cheat > Explore AntiCheat System:*
• Cheat > Game:*
• Cheat > Launcher Abuser
• Cheat > Linux Kernel Explorer
• Cheat > Cheat Engine Plugins
• Some Tricks > Windows Ring0
• Some Tricks > Windows Ring3
• Some Tricks > Linux
• Some Tricks > Android

Escalation Model

User-Mode

• Read and write process memory
• Inject DLLs or shellcode
• Hook graphics or input APIs

Kernel-Mode

• Use signed or vulnerable drivers for direct memory access
• Bypass handle-based protections and inspect protected processes
• Interact with callbacks, page tables, or kernel objects directly

Below the OS

• Virtualize the system with a hypervisor
• Read memory through PCIe DMA hardware
• Move logic to external devices or secondary machines

Core Concepts

Memory Manipulation

• Read Process Memory (RPM)
• Write Process Memory (WPM)
• Pattern scanning
• Pointer chains
• Structure reconstruction

Process Injection

• DLL injection methods
• Manual mapping
• Shellcode injection
• Thread hijacking
• APC injection

Hooking Techniques

• Inline hooking (detours)
• IAT/EAT hooking
• VTable hooking
• Hardware breakpoint hooks
• Syscall hooking

Cheat Categories

Visual Cheats (ESP)

- World-to-Screen transformation
- Player/entity rendering
- Box ESP, skeleton ESP
- Item highlighting
- Radar/minimap hacks

Aim Assistance

- Aimbot algorithms (memory-based and AI visual)
- Triggerbot (auto-fire on crosshair detection)
- No recoil/no spread
- Bullet prediction and lead calculation
- Silent aim (server-side angle manipulation)
- AI visual aimbot (YOLO-based, no memory access required)

AI Visual Cheats (Computer Vision Aimbot)

Architecture overview:
Screen-capture paradigm — uses frame capture, object detection, and input
injection. Some implementations can avoid process attachment, a cheat driver,
and direct game-memory reads; that does not make the full pipeline artifact-free.

Typical setup:
┌─────────────────┐     screen capture      ┌──────────────────┐
│  Gaming PC      │ ───────────────────────▶ │  AI Pipeline     │
│  Game + OBS     │                          │  (same PC, or    │
│                 │ ◀─────────────────────── │   second PC)     │
└─────────────────┘     hardware input       │  YOLO model      │
                        (KMBox / Logitech)   │  TensorRT/CUDA   │
                                             └──────────────────┘

Dual-machine variant (maximum isolation):
- Machine A (game): only runs game + OBS, sends frames via NDI/capture card
- Machine B (cheat): runs AI model, sends mouse commands via USB/network
  to hardware input device on Machine A
- Game machine need not run the model or decision logic, though capture,
  transport, and input-device artifacts can remain

Single-machine variant:
- OBS + AI model run on the same PC
- AI implemented as OBS filter plugin (looks like "OBS is running")
- Mouse output via hardware device or driver-level injection

Pipeline stages:

1. Frame Capture:
   - OBS Game Capture (injects graphics hook DLL into game process)
   - OBS Window Capture (no injection, uses DXGI Desktop Duplication)
   - OBS plugin filter form (AI as OBS filter, minimal footprint)
   - Direct framebuffer copy from GPU output layer (60+ FPS)
   - Capture card (for dual-machine: HDMI/DP input on cheat PC)

2. AI Object Detection:
   - Model: YOLOv5 / YOLOv8 / YOLOv10 / YOLO11 (lightweight variants)
   - Training: fine-tuned on game-specific screenshots
     (enemy bodies, heads, torsos as labeled bounding boxes)
   - Input: cropped region around crosshair (320x320 or 640x640)
     to reduce inference cost
   - Output: bounding boxes with class (head/body/enemy) + confidence score
   - Acceleration: TensorRT (NVIDIA), CUDA, DirectML, OpenVINO
   - Set and measure the latency budget on the target capture path, model,
     hardware, frame rate, and input transport

3. Coordinate Transform and Aiming Logic:
   - Convert pixel coordinates to mouse movement delta:
     delta_x = (target_x - screen_center_x) * sensitivity
     delta_y = (target_y - screen_center_y) * sensitivity
   - Target selection: closest to crosshair, highest confidence,
     head priority, or combined scoring
   - FOV (Field of View) lock: only engage targets within
     configurable pixel radius from crosshair center

4. Attempts to mask automated trajectories:
   - Gradual movement with an acceleration curve instead of an instant snap
   - Synthetic jitter
   - Bézier curve or cubic interpolation for path
   - End-point correction (overshoot then settle)
   - Configurable engagement probability
   - Slight intentional offset (not pixel-perfect center-mass)
   - Variable reaction delay
   These transformations do not establish human equivalence; repeated
   parametric behavior can itself become a feature.

5. Mouse Movement Execution:
   - Hardware input devices (see Input Simulation section below)
   - Movement commands sent as physical HID reports
   - The host receives protocol-conformant HID input rather than a user-mode
     injection API call; device provenance and behavior may still be observable

Why OBS specifically:
- Legitimate streaming software, used by millions of streamers
- Blanket action against OBS-related processes would create substantial
  collateral impact; process presence alone is not attribution
- Game Capture provides fast, low-latency frame access
- Plugin system can host filters inside OBS, but loaded plugins, behavior, and
  surrounding telemetry may still be inspected
- Supports D3D11, D3D12, Vulkan, OpenGL capture paths

YOLO Model Training Pipeline (for Game AI Aimbot)

End-to-end workflow from raw game screenshots to deployed TensorRT model.

1. Data Collection:
   - Capture game screenshots during actual gameplay (OBS recording or replay)
   - Capture diverse scenarios: different maps, lighting, character skins,
     distances, poses, partial occlusion, smoke/flash effects
   - Determine dataset size from coverage and learning curves; image count alone
     does not guarantee robustness
   - Include negative samples (empty scenes, friendlies, environment objects)

2. Annotation / Labeling:
   - Tools: LabelImg (YOLO format), CVAT (collaborative), Roboflow (cloud),
     Label Studio, makesense.ai (browser-based)
   - YOLO format: one .txt per image, each line:
     <class_id> <center_x> <center_y> <width> <height>
     (all values normalized to 0-1 relative to image dimensions)
   - Class definitions (typical):
     0: enemy_body (full body bounding box)
     1: enemy_head (head-only bounding box, for headshot targeting)
     2: friendly (to avoid shooting teammates)
   - Label head separately from body for head-priority targeting
   - Quality control: consistent label boundaries, no missed instances

3. Data Augmentation:
   - Built-in Ultralytics augmentations (mosaic, mixup, copy-paste)
   - Game-specific augmentations:
     - Brightness/contrast variation (simulate different map lighting)
     - Random crop around crosshair area (match inference ROI)
     - Motion blur (simulate fast movement)
     - Noise injection (simulate compression artifacts)
   - Avoid augmentations that distort aspect ratio
     (characters would look unnatural, hurting accuracy)

4. Training:
   - Framework: Ultralytics YOLOv8/v10/v11/YOLO11
   - Base model: yolov8n.pt or yolov8s.pt (nano/small for speed)
     or yolo11n.pt for latest architecture
   - Training command:
     yolo detect train data=game_dataset.yaml model=yolov8n.pt
       epochs=100 imgsz=640 batch=16 device=0
   - dataset.yaml structure:
     path: /path/to/dataset
     train: images/train
     val: images/val
     names: {0: enemy_body, 1: enemy_head, 2: friendly}
   - Key hyperparameters to tune include input size, learning rate, confidence
     threshold, NMS IoU threshold, batch size, and augmentation policy
   - Measure training and inference cost on the exact model, software stack,
     precision, and target hardware

5. Validation and Testing:
   - Evaluate mAP@0.5 and mAP@0.5:0.95 on validation set
   - Choose operating thresholds from precision/recall and downstream error
     costs; no single mAP cutoff establishes reliable deployment
   - Test inference speed on target hardware and evaluate held-out maps, skins,
     patches, capture paths, and hard negatives

6. Export to TensorRT (deployment):
   - Step 1: Export to ONNX
     yolo export model=best.pt format=onnx simplify=True opset=17
   - Step 2: Convert ONNX to TensorRT engine
     yolo export model=best.pt format=engine half=True device=0
     (half=True enables FP16 precision)
   - Or use trtexec directly:
     trtexec --onnx=best.onnx --saveEngine=best.engine
       --fp16 --workspace=4096
   - Benchmark FP16 against FP32 on the exported model; latency, throughput, and
     accuracy changes are hardware- and graph-specific
   - INT8 can improve throughput but requires representative calibration data
     and accuracy validation

7. Runtime Integration:
   - Load TensorRT engine in C++/Python inference loop
   - Input: preprocessed frame (resize, normalize, HWC→CHW, float32/16)
   - Decode the exporter/version-specific output tensor; shapes and NMS
     placement vary across model and runtime versions
   - Apply NMS (Non-Maximum Suppression) to deduplicate detections
   - Select target based on: closest to crosshair + highest confidence
   - Convert pixel coordinates to mouse delta

Alternative acceleration backends:
- DirectML (AMD GPUs, Windows native)
- OpenVINO (Intel GPUs/CPUs)
- ONNX Runtime with CUDA EP (cross-platform)
- CoreML (macOS, less common for game cheats)

Movement Cheats

- Speed hacks
- Fly hacks
- No clip
- Teleportation
- Bunny hop automation

Miscellaneous

- Wallhacks
- Skin changers
- Unlock all
- Economy manipulation

Overlay & Rendering

Overlay Methods

• DirectX Hook: D3D9/11/12 Present hook
• Vulkan Hook: vkQueuePresentKHR hook
• OpenGL Hook: wglSwapBuffers hook
• DWM Overlay: Desktop Window Manager
• External Window: Transparent overlay window
• Steam Overlay: Hijacking Steam's overlay
• NVIDIA Overlay: GeForce Experience hijack

Rendering Libraries

• Dear ImGui: Immediate mode GUI
• GDI/GDI+: Windows graphics
• Direct2D: Hardware-accelerated 2D

Memory Access Methods

User-Mode

- OpenProcess + ReadProcessMemory
- NtReadVirtualMemory
- Memory-mapped files
- Shared memory sections

Kernel-Mode

- Driver-based access
- Physical memory access
- MDL-based copying
- KeStackAttachProcess

Advanced Methods

- DMA (Direct Memory Access)
- EFI runtime services
- Hypervisor-based access
- Hardware-based (FPGA)

EFI/UEFI Cheats

Boot-Time Loading

- EFI manual map: load unsigned driver payload during UEFI boot phase
- ExitBootServices hook: intercept Windows boot to inject kernel code
- Runtime DXE drivers: persist across OS boot via EFI runtime services
- GetVariable/SetVariable: communicate between EFI and OS runtime

EFI-Based Memory Access

- Map physical memory via EFI runtime services
- Bypass DSE entirely (code runs before Windows kernel loads)
- Survive Secure Boot if firmware is compromised or test-signed
- Combine with DMA for maximum stealth

Detection Challenges

- Pre-OS or manually mapped payloads may avoid the normal Windows image-load
  path, so a corresponding driver image callback need not occur
- Artifacts depend on later payload stages; absence from MmUnloadedDrivers or
  PiDDBCacheTable is not guaranteed
- Secure Boot + TPM attestation is primary defense
- Firmware integrity measurement (UEFI capsule verification)

HWID Spoofing

Targets

- Disk serial: IOCTL_STORAGE_QUERY_PROPERTY, SMART data
- NIC MAC address: NDIS OID_802_3_PERMANENT_ADDRESS
- SMBIOS: motherboard serial, system UUID, BIOS vendor
- GPU serial: registry-based or NVAPI/ADL queries
- Monitor EDID: display serial number
- Volume serial: NtQueryVolumeInformationFile
- TPM EK: Endorsement Key fingerprint

Techniques

- Disk filter driver: intercept IOCTL and replace serial in response
- Registry value spoofing: modify cached hardware IDs
- SMBIOS table patching: modify raw SMBIOS memory region
- NIC driver hook: replace MAC in NDIS miniport response
- Full HWID spoofer: coordinated spoofing across all identifiers

Stack Spoofing

Return Address Spoofing

- Replace return address on stack before API call
- Restore original after call returns
- Evades stack-walk-based detection (RtlWalkFrameChain)
- Techniques: JMP RBX gadget, synthetic frames, fiber-based

Call Stack Reconstruction

- Build fake but plausible call stack frames
- Match expected module return addresses (ntdll, kernel32)
- Evade NtQueryInformationThread stack inspection
- Tools: SpoofCallStack, Vulcan, CallStackSpoofer

Detection & Evasion

- Anti-cheat walks thread stacks looking for non-module returns
- Stack unwinding via .pdata / UNWIND_INFO validation
- Spoofed stacks must pass RtlVirtualUnwind consistency checks

Driver Communication

Full Taxonomy (40+ methods in README)

IOCTL-based:
- Standard DeviceIoControl with custom control codes
- Buffered I/O, Direct I/O, METHOD_NEITHER

Data pointer swaps (abusing legitimate syscalls):
- NtUserGetObjectInformation
- NtConvertBetweenAuxiliaryCounterAndPerformanceCounter
- NtUserRegisterRawInputDevices
- NtGdiGetCOPPCompatibleOPMInformation
- NtDxgkGetTrackedWorkloadStatistics
- NtUserGetPointerInfoList
- NtUserSetInformationThread
- NtDCompositionSetChildRootVisual
- Win32k syscall hooks

Shared memory:
- Named shared sections (ZwCreateSection + ZwMapViewOfSection)
- Physical memory mapping
- Shared event objects for signaling

Callback-based:
- Registry callbacks (CmRegisterCallbackEx)
- Minifilter communication ports (FltCreateCommunicationPort)
- Object callbacks with embedded data

Unconventional channels:
- Named pipes from kernel
- Window messages (NtUserPostMessage)
- ETW provider channels
- Socket from kernel (Winsock Kernel / WSK)
- File system filter callbacks
- Debugging APIs (DbgPrint interception)

World-to-Screen Calculation

Basic Formula

Vector2 WorldToScreen(Vector3 worldPos, Matrix viewMatrix) {
    Vector4 clipCoords;
    clipCoords.x = worldPos.x * viewMatrix[0] + worldPos.y * viewMatrix[4] + 
                   worldPos.z * viewMatrix[8] + viewMatrix[12];
    clipCoords.y = worldPos.x * viewMatrix[1] + worldPos.y * viewMatrix[5] + 
                   worldPos.z * viewMatrix[9] + viewMatrix[13];
    clipCoords.w = worldPos.x * viewMatrix[3] + worldPos.y * viewMatrix[7] + 
                   worldPos.z * viewMatrix[11] + viewMatrix[15];
    
    if (clipCoords.w < 0.1f) return invalid;
    
    Vector2 NDC;
    NDC.x = clipCoords.x / clipCoords.w;
    NDC.y = clipCoords.y / clipCoords.w;
    
    Vector2 screen;
    screen.x = (screenWidth / 2) * (NDC.x + 1);
    screen.y = (screenHeight / 2) * (1 - NDC.y);
    
    return screen;
}

Engine-Specific Techniques

Unity (Mono)

• Assembly-CSharp.dll analysis
• Mono JIT hooking
• Il2CppDumper for IL2CPP builds
• Method address resolution

Unity (IL2CPP)

• GameAssembly.dll analysis
• Metadata recovery
• Type reconstruction
• Native hooking

Unreal Engine

• GObjects/GNames enumeration
• UWorld traversal
• SDK generation (Dumper-7)
• Blueprint hooking

Source Engine

• Entity list enumeration
• NetVars parsing
• ConVar manipulation
• Signature scanning

Input Simulation

Software Methods

• SendInput API
• mouse_event/keybd_event
• DirectInput hooking
• Raw input injection
• Driver-based input (mouclass)

Kernel-Level

• Mouse class service callback
• Keyboard filter drivers
• HID manipulation

Hardware Input Devices (for AI Visual Cheats)

Hardware input devices produce genuine HID reports indistinguishable
from ordinary input in the fields of an individual protocol-conformant report.
Descriptors, timing, topology, firmware, and gameplay behavior can still
provide imperfect signals.

KMBox series (KMBox Net, KMBox B Pro, KMBox B+):
- Standalone hardware device connected via USB or network
- Receives mouse/keyboard commands over TCP/UDP or serial
- Generates real USB HID reports to the gaming PC
- Gaming PC sees a standard USB mouse, not API-injected input
- Network variant enables dual-machine setups
- Supports relative movement, absolute positioning, button events
- API: simple serial/network protocol for move(dx, dy), click, etc.

Arduino / Teensy / STM32 microcontroller:
- Custom firmware emulating USB HID device
- Receives commands from cheat PC via serial/USB CDC
- Generates USB HID mouse reports
- Cheapest hardware option, fully customizable
- Leonardo / Pro Micro (ATmega32U4) most common for native USB HID

Logitech driver exploitation:
- Older versions of G HUB / LGS (Logitech Gaming Software) expose
  internal APIs for mouse movement
- ghub_mouse_move() or lgs_mouse_move() via DLL injection into GHUB
- Logitech devices have driver-level whitelist advantage
- Specific driver versions required (newer versions patched)
- No external hardware needed, but driver-version-dependent

Interception driver (interception.sys):
- Open-source keyboard/mouse filter driver
- Intercepts and injects input at driver level
- Commonly used with AI aimbots for zero-hardware-cost injection
- Detectable by anti-cheat (driver signature known)

HDMI/DP KVM-style middleman:
- Hardware device sitting between mouse and PC
- Intercepts real mouse data, injects AI-calculated deltas
- Can preserve much of the expected interface behavior, depending on its USB
  descriptors, timing, topology, and electrical implementation
- Potentially low software footprint but complex hardware setup

Illustrative detection-surface ordering, not a universal ranking:
1. Dedicated hardware — fewer software artifacts, but device and behavior
   signals remain
2. KVM middleman — limited host software footprint if protocol behavior matches
3. Vendor-driver abuse — version and process/module artifacts may be available
4. Known filter driver — driver identity and behavior may be available
5. User-mode injection API — API/call-path telemetry may be available

KMBox Protocol Details

KMBox Net (network variant) — UDP-based protocol:

Packet header (16 bytes, Little-Endian):
Offset  Field      Size   Description
0x00    MAC        4 B    Device UUID (unique per device, used for auth)
0x04    RAND       4 B    Random value or parameter
0x08    INDEXPTS   4 B    Incrementing sequence number (replay protection)
0x0C    CMD        4 B    Command code

Key command codes:
The values below are firmware/API-version examples; verify them against the
exact device implementation before analysis.
Code          Command          Description
0xAF3C2828    connect          Establish connection with device
0xAEDE7345    mouse_move       Direct mouse movement (dx, dy)
0xAEDE7346    mouse_automove   Human-like movement with interpolation
0xA238455A    mouse_beizer     Bézier curve mouse movement
0x9823AE8D    mouse_left       Left button press/release
0x238D8212    mouse_right      Right button press/release
0x97A3AE8D    mouse_middle     Middle button press/release
0xFFEEAD38    mouse_wheel      Scroll wheel
0x123C2C2F    keyboard_all     Keyboard key event

Mouse API functions:
- move(x, y):                 Direct relative movement, no interpolation
- move_auto(x, y, ms):        Interpolated movement over ms milliseconds
- move_beizer(x, y, ms,       Second-order Bézier curve with custom
    x1, y1, x2, y2):          control points for trajectory shaping

Encrypted variants (enc_*):   Same functions with packet-level encryption
                               to resist network packet analysis

Performance:
- Measure command rate, latency distribution, loss, buffering, and jitter on
  the exact firmware, transport, host, and network; fixed figures do not
  transfer across setups

KMBox B / B Pro (serial variant):
- USB CDC serial communication (COM port)
- Baud rate is firmware/configuration-specific (115200 is one common setting)
- Simpler protocol: ASCII or binary command frames
- Benchmark round-trip timing on the deployed serial stack

Physical keyboard/mouse monitoring:
- monitor() function reads real user input from the device
- Enables "pass-through + inject" mode:
  real user input flows through normally,
  AI-calculated deltas are added on top

Arduino / Teensy HID protocol:
- Custom serial command format (typically simple ASCII):
  "M,dx,dy\n"      — mouse move
  "C,button\n"      — click (1=left, 2=right, 3=middle)
  "K,keycode\n"     — keypress
- USB HID report generated by ATmega32U4 (Leonardo)
  or ARM-based Teensy (3.2, 4.0, 4.1)
- HID report descriptor mimics standard mouse:
  buttons (3 bits) + X delta (8-16 bits) + Y delta (8-16 bits)
- No custom driver needed — OS uses generic HID driver

Logitech driver API (exploitable versions):
- G HUB versions prior to certain patches expose internal functions
- Key DLLs: LGS (lcore.dll), G HUB (ghub_mouse.dll or internal APIs)
- ghub_mouse_move(dx, dy) or equivalent internal symbol
- Accessed via DLL injection into GHUB process
  or LoadLibrary + GetProcAddress
- Movement appears as Logitech device input in the HID stack
- Patched in newer G HUB versions; specific version numbers
  circulate in cheat communities

Anti-Detection Techniques

Code Protection

• Polymorphic code
• Code virtualization
• Anti-dump techniques
• String encryption

Runtime Evasion

• Stack spoofing
• Return address manipulation
• Thread context hiding
• Module concealment

Development Workflow

External Cheat

1. Pattern scan for signatures
2. Read game memory externally
3. Process data in separate process
4. Render overlay or use input simulation

Internal Cheat

1. Inject into game process
2. Hook rendering functions
3. Access game objects directly
4. Render through game's graphics context

Learning Resources

Communities

• UnknownCheats
• GuidedHacking
• Game Hacking Academy

Practice Targets

• PWN Adventure (intentionally vulnerable)
• CTF game challenges
• Older/unsupported games

Data Source

Important: This skill provides conceptual guidance and overview information. For detailed information use the following sources:

1. Project Overview & Resource Index

Fetch the main README for the full curated list of repositories, tools, and descriptions:

https://raw.githubusercontent.com/gmh5225/awesome-game-security/refs/heads/main/README.md

The main README contains thousands of curated links organized by category. When users ask for specific tools, projects, or implementations, retrieve and reference the appropriate sections from this source.

2. Repository Code Details (Archive)

For detailed repository information (file structure, source code, implementation details), the project maintains a local archive. If a repository has been archived, always prefer fetching from the archive over cloning or browsing GitHub directly.

Archive URL format:

https://raw.githubusercontent.com/gmh5225/awesome-game-security/refs/heads/main/archive/{owner}/{repo}.txt

Examples:

https://raw.githubusercontent.com/gmh5225/awesome-game-security/refs/heads/main/archive/ufrisk/pcileech.txt
https://raw.githubusercontent.com/gmh5225/awesome-game-security/refs/heads/main/archive/000-aki-000/GameDebugMenu.txt

How to use:

1. Identify the GitHub repository the user is asking about (owner and repo name from the URL).
2. Construct the archive URL: replace {owner} with the GitHub username/org and {repo} with the repository name (no .git suffix).
3. Fetch the archive file — it contains a full code snapshot with file trees and source code generated by code2prompt.
4. If the fetch returns a 404, the repository has not been archived yet; fall back to the README or direct GitHub browsing.

3. Repository Descriptions

For a concise English summary of what a repository does, the project maintains auto-generated description files.

Description URL format:

https://raw.githubusercontent.com/gmh5225/awesome-game-security/refs/heads/main/description/{owner}/{repo}/description_en.txt

Examples:

https://raw.githubusercontent.com/gmh5225/awesome-game-security/refs/heads/main/description/00christian00/UnityDecompiled/description_en.txt
https://raw.githubusercontent.com/gmh5225/awesome-game-security/refs/heads/main/description/ufrisk/pcileech/description_en.txt

How to use:

1. Identify the GitHub repository the user is asking about (owner and repo name from the URL).
2. Construct the description URL: replace {owner} with the GitHub username/org and {repo} with the repository name.
3. Fetch the description file — it contains a short, human-readable summary of the repository's purpose and contents.
4. If the fetch returns a 404, the description has not been generated yet; fall back to the README entry or the archive.

Priority order when answering questions about a specific repository:

1. Description (quick summary) — fetch first for concise context
2. Archive (full code snapshot) — fetch when deeper implementation details are needed
3. README entry — fallback when neither description nor archive is available

Compiled wiki

Prefer the compiled domain overview at wiki/overviews/game-hacking.md (see wiki/index.md and wiki/AGENTS.md) before re-deriving synthesis from raw README/archive material.