# Excuse me, sir? Your language model is leaking (information)

**Authors:** Or Zamir  
**arXiv:** [2401.10360](https://arxiv.org/abs/2401.10360)  
**Date:** 2024-01-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.10360v1/

## Key points

- Novel cryptographic method for concealing secret payloads within a Large Language Model (LLM)
- Utilizes a secret key to extract hidden payload from the model's response
- Impossible to differentiate between original LLM responses and those with hidden payload without the key
- Builds upon prior breakthrough in undetectable watermarking scheme for LLMs
- Allows for secure embedding of arbitrary secret information within LLM responses
- Significant implications for preserving data privacy and security when using large language models
- Does not compromise the quality of generated text while concealing sensitive information within LLMs

## Summary

In their paper titled "Excuse me, sir? Your language model is leaking (information)", Or Zamir and his team introduce a novel cryptographic method for concealing secret payloads within the response of a Large Language Model (LLM). This method utilizes a secret key to extract the hidden payload from the model's response. Importantly, it has been proven that without this key, it is impossible to differentiate between the responses of the original LLM and the one that hides a payload. Furthermore, this technique does not compromise the quality of generated text. The authors build upon a recent breakthrough by Christ, Gunn, and Zamir in 2023 who introduced an undetectable watermarking scheme for LLMs. By extending this prior work, Zamir et al. have developed an innovative approach that allows for secure embedding of arbitrary secret information within LLM responses. This research has significant implications for preserving data privacy and security when utilizing large language models. The paper introduces a new cryptographic method for hiding secret payloads within Large Language Models (LLMs). The research focuses on protecting data privacy and security while utilizing large language models. The proposed method allows for secure embedding of arbitrary secret information within LLM responses. The study offers enhanced protection against information leakage in large language models to preserve data privacy and security. The technique does not compromise the quality of generated text while concealing sensitive information within large language models.