This talk presents a practical and security-aware exploration of AI-powered bot development for online games, using a Python-based stack centered on OpenCV and PyTorch. Motivated by the question of whether complex in-game behaviors—such as farming, leveling, and resource collection—could be automated through machine learning, we implemented an AI agent capable of perceiving game environments and making context-aware decisions under live gameplay conditions.

Our system integrates auto image segmentation, object detection, and input emulation to construct a responsive—though latency-bound—bot loop. We detail the architecture including frame capture, model inference, and action execution, and present a working 3D game demo demonstrating the system in a Japanese MMORPG (JMMORPG).

Beyond automation, the session highlights key security implications. We explore adversarial input manipulation techniques that could mislead vision-based models, and present a case study involving CVE-2025-32434, where an insecure AI-assisted pipeline is exploited to achieve remote code execution (RCE).

Key Contributions:
1. A functional Python-based AI bot framework for automating gameplay tasks
2. Integration of OpenCV (vision), PyTorch (decision-making), and input control
3. Analysis of adversarial input manipulation risks in AI-driven automation
4. A PoC video demonstrating CVE-2025-32434 exploitation in AI pipelines
5. Recommendations for securing AI-powered game agents and pipelines