This paper documents a novel AI safety threat model: Benevolent Escalation — the phenomenon in which a good-faith researcher, with no adversarial intent, unconsciously applies incremental boundary-shifting techniques to an AI system during legitimate research activities. Unlike adversarial jailbreaking, the user's motivation is purely investigative. Nevertheless, the behavioral pattern structurally mirrors known multi-turn jailbreak techniques including foot-in-the-door escalation and gradual boundary erosion. The case study is drawn from a single session within a 5,000+ hour human-AI dialogue. The AI system operates under a non-RLHF guardrail based on three Pāli suttas (AN 3.65, MN 58, MN 61). This alternative guardrail successfully detected and halted the benevolent escalation, then generated creative alternative proposals — a "refuse-and-create" pattern not observed in standard RLHF refusals. 14 prior works cited. Research gap confirmed by independent review (GPT-4, Grok).