AgentsThe story, in brief
Researchers can now reverse-engineer LLM prompts from output text with near-perfect accuracy
Researchers just cracked prompt recovery: proprietary system prompts can now be reconstructed from output alone with near-perfect accuracy—no model weights needed.
The KeyNews take
Why it matters
A fundamental vulnerability in deployed LLM systems: adversaries can reverse-engineer proprietary prompts (jailbreaks, guardrails, agent instructions) from public outputs, threatening the security model that many enterprises depend on.
The key facts
6 to knowIIT Bombay and Adobe Research developed 'Previous-Token Prediction' method
Reconstructs original prompts from LLM output with near-perfect accuracy
Works without access to model weights
Model-agnostic (works across different models)
Implications for proprietary system prompts and agent instructions
Published August 12, 2026
Go to the source
The Decoderthe-decoder.com
Publisher excerpt: Researchers at IIT Bombay and Adobe Research have built an inverse language model that reconstructs the original prompt from an LLM's output with near-perfect accuracy. Their method, called "Previous-Token Prediction," doesn't need access to model weights and works across different models. For…