用大模型在聊天中隐秘传输加密信息，防监控且兼容各类模型。
An LLM Framework For Cryptography Over Chat Channels
- 利用大模型生成类人对话文本，嵌入加密信息
- 支持公钥与对称密钥加密，隐蔽性高且不依赖特定模型
- 适合在审查严格环境下进行安全通信，研究人员和隐私保护者可参考
大型语言模型（LLM）的发展已深刻改变通信方式，但其在安全通信中的应用仍不充分，尤其是在监控严密的环境中。全球多国正推动立法以检测、植入后门甚至禁止加密通信，凸显了在开放渠道中实现安全、隐蔽通信的迫切需求。本文提出一种新型密码嵌入框架，可在公开聊天频道中实现公钥或对称密钥加密通信，同时生成类人对话文本。该框架具备三大特性：1. 模型无关性，通信双方可独立使用不同本地LLM；2. 抗量子无关性，不依赖特定密码体系；3. 与人类生成的聊天文本难以区分。该方案为传统加密被检测或禁用的场景提供了可行替代方案。
原文摘要 · Abstract (English)
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.
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