The author proposes a "Harvard/von Neumann Jihad" to address vulnerabilities in AI systems, particularly Large Language Models (LLMs), stemming from the mixing of commands and data. They draw a parallel to earlier security issues like phone phreaking and SQL injection, suggesting that separating commands from data, as in a Harvard architecture, could be a solution. However, the author expresses doubt about the feasibility of securing LLMs due to the inherent nature of natural language processing, questioning if a safe solution exists beyond simple text filtering. AI
影响 Proposes a conceptual architectural shift to enhance LLM security against prompt injection attacks.
排序理由 The item is an opinion piece discussing potential security vulnerabilities in LLMs and proposing a conceptual solution.
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