arXiv Computation and Language October 23, 2026 By Om Tailor, Yucheng Pan, Solaleh Mohammadi, Xinyan Liu, Kaiqing Zhang

Beyond End-to-End Recovery: Towards a Better Understanding of LLM Steganography via Rolewise Decomposition

Read the original on arXiv Computation and Language →

The paper introduces a rolewise decomposition framework to analyze large language model steganography beyond traditional end‑to‑end recovery metrics. By separately examining how private messages are encoded in the text, what decoders actually recover, and what can be deterministically verified, the authors uncover hidden issues in the encoding and decoding processes. Their study of 7,200 encoder responses reveals 168 verifiable encodings missed by decoders and 706 responses where private messages were reconstructed via unintended patterns, highlighting limitations of end‑to‑end evaluation.

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