---
title: "Researchers reconstruct LLM prompts from outputs, near-perfect accuracy"
url: https://www.parallelquant.com/posts/researchers-reconstruct-llm-prompts-from-outputs-near-perfect-accuracy-33796c
source_name: "The Decoder"
source_url: https://the-decoder.com/researchers-can-now-reverse-engineer-llm-prompts-from-output-text-with-near-perfect-accuracy/
published: 2026-08-12T17:32:59.000Z
topics: ["research", "security"]
publisher: "Parallel Quant"
---

# Researchers reconstruct LLM prompts from outputs, near-perfect accuracy

*2026-08-12 · Source: [The Decoder](https://the-decoder.com/researchers-can-now-reverse-engineer-llm-prompts-from-output-text-with-near-perfect-accuracy/)*

Researchers at IIT Bombay and Adobe Research built an inverse language model, called Previous-Token Prediction, that reconstructs a model's original prompt from its output text alone. The technique needs no access to model weights and works across different models.

**Why it matters:** Companies that treat system prompts as proprietary IP or a security layer now face a concrete extraction technique, not just a theoretical risk. Expect this to accelerate interest in prompt-obfuscation and output-filtering defenses for commercial large language model (LLM) products.

**Topics:** research, security

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Read the original: https://the-decoder.com/researchers-can-now-reverse-engineer-llm-prompts-from-output-text-with-near-perfect-accuracy/
Canonical: https://www.parallelquant.com/posts/researchers-reconstruct-llm-prompts-from-outputs-near-perfect-accuracy-33796c
Published by Parallel Quant — https://www.parallelquant.com