Prompt Injection (PI) represents a fundamental shift in the security landscape of Large Language Models (LLMs). This analysis traces PI's evolution through interactive gaming environments, examining the transition from foundational "Attention Hijacking" in Gandalf and "Persona Adoption" in Tensor Trust to advanced "Token Smuggling" in AI Dungeon. The study culminates in Indirect Prompt Injection, demonstrating how aggressive instructions embedded within blockchain metadata can silently hijack autonomous agents. Crucially, this article also explores how PI principles can be leveraged to make games more engaging, unpredictable, and challenging. By synthesizing interactive mechanics with transformer architecture, this article provides a technical roadmap for understanding the next generation of AI-driven exploits inherent in unified instruction-data streams.
Introduction "The limits of my language mean the limits of my world." When Ludwig Wittgenstein penned this in 1921, he was delineating t