This research introduces a stochastic dynamical framework that models how LLMs can unintentionally amplify harmful content through their own reasoning processes.

• Models LLM reasoning as a continuous-time stochastic process with measurable severity variables
• Conceptualizes harmful content generation as a critical threshold phenomenon
• Provides mathematical tools to identify and prevent toxicity self-amplification
• Establishes foundation for more robust security guardrails in LLM systems

For security teams, this framework offers a principled approach to anticipate and mitigate cases where an LLM might generate increasingly harmful outputs through extended reasoning chains - a critical advancement for deploying safer AI systems.

A Stochastic Dynamical Theory of LLM Self-Adversariality: Modeling Severity Drift as a Critical Process