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Prior work demonstrated this threat but relied on trivially recoverable encodings. We formalize payload recoverability via classifier accuracy and show previous schemes achieve 100\\% recoverability. In response, we introduce low-recoverability steganography, replacing arbitrary mappings with embedding-space-derived ones. For Llama-8B (LoRA) and Ministral-8B (LoRA) trained on TrojanStego prompts, exact secret recovery rises from 17$\\rightarrow$30\\% (+78\\%) and 24$\\rightarrow$43\\% (+80\\%) respectively, while on Llama-70B (LoRA) trained on Wiki prompts, it climbs from 9$\\rightarrow$19\\% (+123\\%), all while reducing payload recoverability. We then discuss detection. We argue that detecting fine-tuning-based steganographic attacks requires approaches beyond traditional steganalysis. Standard approaches measure distributional shift, which is ","title":"Hide and Seek in Embedding Space: Geometry-based Steganography and Detection in Large Language Models","url":"https://arxiv.org/abs/2601.22818","vendor":"arxiv_cs_ai"},"summary":"arXiv:2601.22818v2 Announce Type: replace-cross \nAbstract: Fine-tuned LLMs can covertly encode prompt secrets into outputs via steganographic channels. Prior work demonstrated this threat but relied on trivially recoverable encodings. We formalize payload recoverability via classifier accuracy and show previous schemes achieve 100\\% recoverability. In response, we introduce low-recoverability steganography, replacing arbitrary mappings with embedding-space-derived ones. For Llama-8B (LoRA) and Ministral-8B (LoRA) trained on TrojanStego prompts, exact secret recovery rises from 17$\\rightarrow$30\\% (+78\\%) and 24$\\rightarrow$43\\% (+80\\%) respectively, while on Llama-70B (LoRA) trained on Wiki prompts, it climbs from 9$\\rightarrow$19\\% (+123\\%), all while reducing payload recoverability. We then discuss detection. We argue that detecting fine-tuning-based steganographic attacks requires approaches beyond traditional steganalysis. 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