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New method reconstructs LLM prompts from output text with high accuracy

Researchers have developed a new method capable of reconstructing the original prompts used to generate text from large language models (LLMs) with high accuracy. This technique, dubbed "Previous-Token Prediction," functions without requiring access to the LLM's internal weights and has demonstrated effectiveness across various models. The development poses a potential security concern for organizations that utilize proprietary system prompts, as it could expose sensitive instructions. AI

IMPACT This technique could expose proprietary system prompts, posing a security risk for organizations using LLMs.

RANK_REASON Academic research paper detailing a new method for LLM prompt reconstruction. [lever_c_demoted from research: ic=1 ai=1.0]

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

New method reconstructs LLM prompts from output text with high accuracy

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Academic research paper detailing a new method for LLM prompt reconstruction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. The Decoder TIER_1 English(EN) · Matthias Bastian ·

    Researchers can now reverse-engineer LLM prompts from output text with near-perfect accuracy

    <p><img alt="" class="attachment-full size-full wp-post-image" height="768" src="https://the-decoder.com/wp-content/uploads/2026/08/prompt_extracting.png" style="height: auto; margin-bottom: 10px;" width="1376" /></p> <p> Researchers at IIT Bombay and Adobe Research have built an…