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Anthropic explains LLM's challenge in distinguishing own thoughts from user input

Anthropic published a technical explanation of how LLMs like Claude perceive conversation as a single continuous text stream, making it difficult to distinguish between their own generated text and user input. The post uses a snapshot of Claude's response to illustrate the problem, highlighting the fundamental difference between the structured chat interface users see and the raw token sequence the model processes.

0 engagement·1 source·Thu, Jul 9, 2026, 07:07 PM
In a post titled 'A Mechanistic Explanation of Prompt Injection (and why you should study roles)' on LessWrong, Anthropic explains that LLMs receive input as a single, continuous stream of text containing system prompts, user messages, tool outputs, and the model's own previous responses. This lack of structural distinction makes it inherently difficult for the model to differentiate between its own generated text and user-provided text, which is a root cause of prompt injection vulnerabilities. The post includes a snapshot of Claude's response midway through generation to illustrate the point.

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Anthropic(company)Claude(model)LLM(concept)prompt injection(concept)

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