# An LLM Framework For Cryptography Over Chat Channels

arXiv: https://arxiv.org/abs/2504.08871 · HTML: https://mlsanity.com/paper/2504.08871/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 11 Apr 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://mlsanity.com/paper/2401.02954/) (2024)
- [Undetectable Watermarks for Language Models](https://mlsanity.com/paper/2306.09194/) (2023)
- [A Survey of Text Watermarking in the Era of Large Language Models](https://mlsanity.com/paper/2312.07913/) (2023)
- [REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Models](https://mlsanity.com/paper/2310.12362/) (2023)
- [No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices](https://mlsanity.com/paper/2402.16187/) (2024)
- [Watermarking Techniques for Large Language Models: A Survey](https://mlsanity.com/paper/2409.00089/) (2024)
- [Watermarking Language Models for Many Adaptive Users](https://mlsanity.com/paper/2405.11109/) (2024)
- [Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding](https://mlsanity.com/paper/2010.00677/) (2020)
- [SparSamp: Efficient Provably Secure Steganography Based on Sparse Sampling](https://mlsanity.com/paper/2503.19499/) (2025)
- [Excuse me, sir? Your language model is leaking (information)](https://mlsanity.com/paper/2401.10360/) (2024)
- [OD-Stega: LLM-Based Relatively Secure Steganography via Optimized Distributions](https://mlsanity.com/paper/2410.04328/) (2024)
- [Multi-Designated Detector Watermarking for Language Models](https://mlsanity.com/paper/2409.17518/) (2024)
- [Dynamically Allocated Interval-Based Generative Linguistic Steganography with Roulette Wheel](https://mlsanity.com/paper/2401.15656/) (2024)
- [SoK: Decentralized Randomness Beacon Protocols](https://mlsanity.com/paper/2205.13333/) (2022)
- [Can ChatGPT Learn to Count Letters?](https://mlsanity.com/paper/2502.16705/) (2025)
- [Shifting-Merging: Secure, High-Capacity and Efficient Steganography via Large Language Models](https://mlsanity.com/paper/2501.00786/) (2025)

## Abstract (arXiv)

Recent advancements in Large Language Models (LLMs) have transformed communication, yet their role in secure messaging remains underexplored, especially in surveillance-heavy environments. At the same time, many governments all over the world are proposing legislation to detect, backdoor, or even ban encrypted communication. That emphasizes the need for alternative ways to communicate securely and covertly over open channels. We propose a novel cryptographic embedding framework that enables covert Public Key or Symmetric Key encrypted communication over public chat channels with humanlike produced texts. Some unique properties of our framework are: 1. It is LLM agnostic, i.e., it allows participants to use different local LLM models independently; 2. It is pre- or post-quantum agnostic; 3. It ensures indistinguishability from human-like chat-produced texts. Thus, it offers a viable alternative where traditional encryption is detectable and restricted.