Steganography with Large Language Models

Key Sensitivity Analysis

Abstract

Large language model (LLM) steganography generates fluent cover text that encodes a secret message, with the secret key often given as a natural language prompt or seed. Recent rank based LLM stegosystems achieve high capacity and strong distributional indistinguishability, but little is known about how similar keys affect the stegotext. Cryptographically, we seek an avalanche effect: small changes in the key should induce large, unpredictable changes so that nearby keys do not yield correlated outputs. We present an empirical study of key sensitivity for a representative rank based LLM stegosystem following Norelli and Bronstein, defining several distance metrics between stegotexts and disagreement profiles over token positions. Using a fixed LLM with synthetic prompts and text from Alice's Adventures in Wonderland, we sweep over key pairs to relate key and stegotext distances. Across conditions, even modest key perturbations push the stegotext distances near maximal values, with weak dependence on key difference and roughly uniform sensitivity. For this scheme, the mapping from keys to stegotext behaves qualitatively like a cryptographic primitive in its key coordinate, reinforcing security against distance based or key interpolation attacks and underscoring the need for precise key management.