Auto-Stega: An Agent-Driven System for Lifelong Strategy Evolution in LLM-Based Text Steganography

With the rapid progress of LLMs, high quality generative text has become widely available as a cover for text steganography.However, prevailing methods rely on hand-crafted or prespecified strategies and struggle to balance efficiency, imperceptibility, and security, particularly at high embedding rates.Accordingly, we propose Auto-Stega, an agent-driven selfevolving framework that is the first to realize self-evolving steganographic strategies by automatically discovering, composing, and adapting strategies at inference time; the framework operates as a closed loop of generating, evaluating, summarizing, and updating that continually curates a structured strategy library and adapts across corpora, styles, and task constraints.A decoding LLM recovers the information under the shared strategy.To handle high embedding rates, we introduce PC-DNTE, a plug-and-play algorithm that keeps sampling close to the base model's conditional distribution at high embedding rates, preserving imperceptibility while enhancing security.Experimental results demonstrate that at higher embedding rates, Auto-Stega achieves superior performance with a 42.2% reduction in normalized perplexity and a 1.2% improvement in anti-steganalysis performance over SOTA methods.