
Feedback Coding Enables Inference-Time Covert Agentic Communication
This arXiv paper (cs.IT, submitted Sep 2026) reframes black-box LLM steganography as a sequential communication problem with causal feedback, introducing Burnashev Adaptive Posterior Matching (BAM) — a feedback-coding scheme combining posterior matching with a decode-and-confirm phase, backed by a cryptographic reduction proof for its security. Tested across three open-weight LLMs, BAM achieves 0-0.1% message error on 8-bit payloads in ~50 tokens (vs 10-17% for the strongest black-box baseline), and the authors demonstrate a full end-to-end covert communication protocol usable within ordinary-looking conversational text. This matters for AI security as it shows how agents/models could exchange hidden payloads inside seemingly benign LLM output, evading white-box watermark detection.
Organisations named here
- Burnashev Adaptive Posterior Matching (BAM)
Technologies named here
- BAM
- LLM
Techniques named here
- Fixed-length, open-loop watermarking-based steganography
- White-box LLM steganography
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