# Synchronized Logit Steering: Real-world Steganography

arXiv: https://arxiv.org/abs/2608.14697 · HTML: https://mlsanity.com/paper/2608.14697/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 9 Aug 2026.

## Builds on (most cited first, 14 of 14)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Neural Linguistic Steganography](https://mlsanity.com/paper/1909.01496/) (2019)
- [Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs](https://mlsanity.com/paper/2410.03768/) (2024)
- [Generative Text Steganography with Large Language Model](https://mlsanity.com/paper/2404.10229/) (2024)
- [The Steganographic Potentials of Language Models](https://mlsanity.com/paper/2505.03439/) (2025)
- [Robust Steganography from Large Language Models](https://mlsanity.com/paper/2504.08977/) (2025)
- [Semantic Steganography: A Framework for Robust and High-Capacity Information Hiding using Large Language Models](https://mlsanity.com/paper/2412.11043/) (2024)
- [Large Language Models as Carriers of Hidden Messages](https://mlsanity.com/paper/2406.02481/) (2024)
- [Auto-Stega: An Agent-Driven System for Lifelong Strategy Evolution in LLM-Based Text Steganography](https://mlsanity.com/paper/2510.06565/) (2025)
- [A high-capacity linguistic steganography based on entropy-driven rank-token mapping](https://mlsanity.com/paper/2510.23035/) (2025)
- [Safeguarding LLMs Against Misuse and AI-Driven Malware Using Steganographic Canaries](https://mlsanity.com/paper/2603.28655/) (2026)

## Abstract (arXiv)

Steganography in large language models offers a way to embed hidden messages within natural-sounding text. Existing token and logit-level methods typically require the sender and receiver to share an identical prompt context, which is rarely guaranteed in production pipelines that use retrieval-augmented generation or proprietary system instructions. We introduce Synchronized Logit Steering (SLS), a deterministic steganographic scheme that eliminates this dependency by deriving a proxy prompt from the generated output itself, allowing both parties to reconstruct the same logit distribution without access to the original prompt. SLS encodes payload values as token ranks within high-entropy regions of the proxy prompt distribution, and we extend the scheme with periodic recurrence and payload bursts to scale information density. Across ShareGPT, GSM8K, and SWE-bench Verified, we show that the KL divergence between the true and proxy prompt distributions falls below 0.5 nats once the synchronization window reaches 40 tokens, and SLS encoding does not meaningfully disrupt this convergence relative to greedy generation. We also find that the periodic-burst variant achieves 0.20 bits per token, or roughly 10x the capacity of single-payload encoding. Kolmogorov-Smirnov tests further confirm that SLS outputs are statistically difficult to distinguish from greedy generations, demonstrating that covert, prompt-agnostic communication through LLMs is both practical and stealthy.