ILSP: Illusory Safety Prompt Why "Do Not Hallucinate" Does Not Work: Definition, Typology, and Literacy Framework

A Call for Collaborative Action on AI Literacy and Safety By sharing this research, we hope to shed light on a pervasive yet often overlooked issue in human-AI interaction. Our sincere wish is that this paper not only raises awareness about the "Illusory Safety Prompt (ILSP)" phenomenon but also inspires fellow researchers, developers, and educators to join forces. By understanding the psychological and structural roots of these illusory instructions, we can work together to build more robust AI literacy frameworks and genuinely effective AI safety standards. We welcome open discussions, critiques, and future collaborations to solve this challenge together. Abstract The proliferation of large language models (LLMs) has produced a widespread phenomenon in which users and organizations deploy prompt instructions purported to enhance AI reliability, truthfulness, or safety — yet produce no measurable change in model behavior. This paper introduces the concept of the Illusory Safety Prompt (ILSP) and provides the first systematic account of its definition, five-category typology, psychological and linguistic mechanisms, empirical evidence, detection criteria, and response literacy framework. We present three converging layers of evidence: (1) Apple's inclusion of "Do not hallucinate" in Apple Intelligence system prompts (macOS 15.1 beta, August 2024), discovered by Reddit user u/devanxd2000 and widely reported by technology press; (2) a Korean cross-platform case in which a single ILSP post accumulated over 105,000 views and 851 comments across three demographically distinct communities within 24 hours (February 25–26, 2026), exhibiting source laundering, prompt mutation, and group-level norm formation that suppresses critical evaluation; and (3) prior academic work mathematically demonstrating the impossibility of eliminating LLM hallucination through prompt instruction alone (Xu et al., 2024). Together, these layers show that ILSPs are not isolated user errors but a structural, cross-sector phenomenon requiring AI literacy education and industry-level standards. Additionally, we present an autoethnographic observation (§7.4) documenting qualitative differences between ILSP-based and context-driven approaches in a real-world AI identity loss scenario, conducted by the co-author (Thea Kim) based on long-term multi-platform AI interaction experience, offered as an illustrative case rather than a controlled study. Furthermore, we provide a pedagogical analysis grounding the response literacy framework in established educational theories — misconception correction (Posner et al., 1982), metacognition (Flavell, 1979), Illich's critique of tool dependency (1973), and informal learning vulnerability — arguing that ILSP is not a failure of technical knowledge but a structurally embedded educational phenomenon requiring constructivist intervention design.