game-hacking-techniques

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 [email protected] and [email protected]: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 o

---

*Content truncated.*