This article explores the nature of authority and interpretation in Large Language Models (LLMs) by drawing parallels to Lewis Carroll's "Through the Looking-Glass." Unlike programming languages with formally defined semantics, LLMs interpret natural language through context, meaning they lack intrinsic authority. Prompts are not programs but attempts to establish context, and instructions within them are not mechanically enforced. This ambiguity makes LLMs vulnerable to prompt injection, where new language competes with and redefines the intended task, potentially leading to security breaches if the model has access to sensitive information. The author suggests that true security boundaries must be imposed externally to the LLM, outside its conversational context, to prevent reinterpretation and circumvention. AI
IMPACT Highlights the need for robust external security measures for LLM-based systems due to their inherent lack of enforced authority.
RANK_REASON The item is an opinion piece discussing the nature of LLMs and their authority, using literary references.
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