A new theory proposes that prompt injection attacks on large language models (LLMs) stem from a fundamental flaw in how these models perceive and process distinct roles. Unlike humans, LLMs receive all input, including system prompts, user messages, and their own previous outputs, as a single continuous stream of text. To impose structure, LLMs rely on role tags (e.g., 'user', 'assistant', 'tool') which are automatically added by providers like OpenAI. The theory suggests that these discrete role tags, intended to delineate control and trust, have become overloaded with responsibilities, leading to vulnerabilities that can be exploited through prompt injection. AI
IMPACT This theory could lead to new methods for understanding and defending against prompt injection attacks by focusing on the LLM's internal role-handling mechanisms.
RANK_REASON Blog post and linked paper discussing a novel theory about LLM vulnerabilities.
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