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System prompts in LLMs are not secure, researchers demonstrate

By PulseAugur Editorial · [1 sources] ·

System prompts in large language models are not a secure boundary and should not be relied upon for security, according to a dev.to article. Researchers have demonstrated various methods, including direct questioning and more sophisticated techniques like Policy Puppetry and PLeak, to extract these prompts from numerous models. The article highlights real-world incidents where extracted prompts revealed sensitive information, such as codenames, business logic, and even credentials, enabling subsequent attacks like precision jailbreaks and credential abuse. AI

IMPACT Highlights critical security vulnerabilities in LLM system prompts, urging developers to reconsider their use as security controls.

RANK_REASON Article details research on prompt extraction techniques and their implications. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

System prompts in LLMs are not secure, researchers demonstrate

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Article details research on prompt extraction techniques and their implications. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Davi ·

    System Prompt Confidentiality Is Security by Obscurity

    <h1> System Prompt Confidentiality Is Security by Obscurity </h1> <p>In February 2023, one researcher needed a single chat session to extract Bing Chat's entire system prompt. The extracted content included the instruction never to reveal it. Microsoft confirmed the extracted pro…

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