An LLM Framework For Cryptography Over Chat Channels
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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.
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.
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19 of 50 references.
References · 19
- 2017Attention Is All You Needcited by 60,361
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
- 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,184
- 2024DeepSeek LLM: Scaling Open-Source Language Models with Longtermismcited by 642
- 2023Undetectable Watermarks for Language Modelscited by 226
- 2023A Survey of Text Watermarking in the Era of Large Language Modelscited by 112
- 2023REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Modelscited by 78
- 2024No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choicescited by 50
- 2024Watermarking Techniques for Large Language Models: A Surveycited by 23
- 2024Watermarking Language Models for Many Adaptive Userscited by 18
- 2020Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Codingcited by 17
- 2025SparSamp: Efficient Provably Secure Steganography Based on Sparse Samplingcited by 14
7 more
- 2024Excuse me, sir? Your language model is leaking (information)cited by 13
- 2024OD-Stega: LLM-Based Relatively Secure Steganography via Optimized Distributionscited by 5
- 2024Multi-Designated Detector Watermarking for Language Modelscited by 4
- 2024Dynamically Allocated Interval-Based Generative Linguistic Steganography with Roulette Wheelcited by 3
- 2022SoK: Decentralized Randomness Beacon Protocolscited by 2
- 2025Can ChatGPT Learn to Count Letters?cited by 2
- 2025Shifting-Merging: Secure, High-Capacity and Efficient Steganography via Large Language Modelscited by 1
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- 2025CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenges
- 2026Feedback Coding Enables Inference-Time Covert Agentic Communication
- 2025A Survey: Towards Privacy and Security in Mobile Large Language Models
- 2023Let Models Speak Ciphers: Multiagent Debate through Embeddings
- 2025Should LLM Safety Be More Than Refusing Harmful Instructions?
- 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly