Personality drift in Large Language Models (LLMs) poses a textitsafety and alignment risk: a model that shifts traits across a dialogue can produce inconsistent or harmful behaviours. Yet most existing psychometric evaluations probe LLMs in a context‑free vacuum, answering each item in isolation—what we call the textitDisney World test. We introduce the first textitcontext‑aware framework that transforms conversational history into a rigorous stress test for persona stability. Our method (i) simulates realistic multi‑turn interactions, (ii) proposes new consistency metrics to quantify alignment‑critical trait drift, and (iii) red‑teams models via prompt inconsistency factors. Across 7 frontier and open LLMs, conversational context boosts answer consistency through in‑context learning but also triggers notable personality shifts: textttGPT‑3.5‑Turbo and textttGPT‑4‑Turbo show the most extreme deviations. We find that textttGPT models remain robust to question ordering, whereas textttGemini‑1.5‑Flash and textttLlama‑3.1‑8B are highly order‑sensitive. Our causal analysis suggests textttGPT responses blend intrinsic persona signals with conversational cues, while textttGemini‑1.5‑Flash and textttLlama‑3.1‑8B rely predominantly on recent context, a potential vulnerability for adversarial steering. We further validate on Role‑Playing Agents, demonstrating that context‑aware alignment yields responses rated more consistent and human‑aligned. Our open‑sourced toolkit enables practitioners to diagnose and monitor persona‑driven safety risks before deployment.