arXiv:2601. 22818v2 Announce Type: replace-cross Abstract: Fine-tuned LLMs can covertly encode prompt secrets into outputs via steganographic channels.
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arXiv:2606. 09411v1 Announce Type: cross Abstract: Large language models can be fine-tuned to encode prompt-borne secrets into fluent, seemingly benign outputs.
arXiv:2606. 09135v1 Announce Type: cross Abstract: We demonstrate that widely deployed Large Language Model (LLM) inference stacks harbor a steganographic channel that requires no modification to model weights, sampling code, or output distributions.
arXiv:2606. 08403v1 Announce Type: cross Abstract: Text-centered prompt-injection defenses assume that the malicious signal is visible in one of the inspected text views.
arXiv:2608. 14697v1 Announce Type: new Abstract: Steganography in large language models offers a way to embed hidden messages within natural-sounding text.
arXiv:2605. 26595v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are often fine-tuned on uncurated text datasets that adversaries can poison.
This survey reviews 148 linguistic steganographic methods, 60 countermeasures, 23 evaluation metrics, and 9 open challenges, providing taxonomies, reviews, and adoption analyses. It identifies five paradigm shifts brought by large language models: moving from covertext modification to prompt-only generation, from heuristic to provable security, from white-box symmetric models to black-box or asymmetric access, from security-centric designs to joint optimization, and from text-quality concerns to engineering issues. The paper aims to serve as a reference and roadmap for practical and responsible linguistic steganography in the LLM era.