Rank-Token Mapping Steganography in LLM Chatbots: Evaluating Capacity and Performance Across Multiple LLMs

Abstract

This paper presents a new steganographic algorithm capable of concealing information within generated text by leveraging conditional probability distributions of tokens and mapping token ranks to encoded data. The proposed solution is implemented as a chat-based graphical user interface (GUI), enabling seemingly standard, natural conversations while simultaneously transmitting hidden secrets within the chatbot’s responses. The study evaluates steganographic capacity and functional performance across various Large Language Models (e.g., LLaMA and Gemma). We investigate how varying the probability threshold and task complexity affects text coherence and code generation using functional correctness benchmarks.