  Linguistic Steganography (LS) tasks aim to generate steganographic text
(stego) based on secret information. Only authorized recipients can perceive
the existence of the stegos and extract secrets, thereby preserving privacy.
However, existing LS methods do not consider the controllable generation of
stegos containing specific discourses such as style, genre, and theme. And they
are difficult to simulate high-quality natural texts. As a result, the stegos
are easily perceived and detectable, compromising covert communication. This
paper proposes the LLsM, the first LS work with the Large Language Model (LLM).
Regarding open-source LLMs, we reconstruct the token generator of LLM to the
"stego generator" so that it can control the generation of stego based on the
secret. In this "stego generator", the candidate pool is encoded by range
coding, and the adjustment factor for the interval length is also given. The
secret determines the interval, thereby determining the next token. This better
simulates the distribution of natural texts and controls the adjustment of the
embedding rate. In addition, we preliminarily built an LLsM-c architecture for
closed-source LLMs. It encodes discourse to obtain high-quality prompts
containing discourse based on secrets, and generates pure natural texts
containing discourse. Experiments show that LLsM performs superior to prevalent
LS and related-task baselines regarding various kinds of concealment and
anti-steganalysis. LLsM's MAUVE surpasses baselines by 60%-80% and
anti-steganalysis exceeds baselines by 20%-30%. Notably, LLsM can also generate
longer stegos with high quality, showing its advantages in understanding and
coherence.
