Toward a Dynamic Stackelberg Game-Theoretic Framework for Agentic AI Defense Against LLM Jailbreaking

This paper proposes a game-theoretic framework that models the interaction between prompt engineers and large language models (LLMs) as a two-player extensive-form game coupled with a Rapidly-exploring Random Trees (RRT) search over prompt space. The attacker incrementally samples, extends, and tests prompts, while the LLM chooses to accept, reject, or redirect, leading to terminal outcomes of Safe Interaction, Blocked, or Jailbreak. Embedding RRT exploration inside the extensive-form game captures both the discovery phase of jailbreak strategies and the strategic responses of the model. Furthermore, we show that the defender’s behavior can be interpreted through a local Stackelberg equilibrium condition, which explains when the attacker can no longer obtain profitable prompt deviations and provides a theoretical lens for understanding the effectiveness of our Purple Agent defense. The resulting game tree thus offers a principled foundation for evaluating, interpreting, and hardening LLM guardrails.