https://x.com/rohanpaul_ai/status/1982222641345057263

The paper shows how an LLM can hide a full message inside another text of equal length.

It runs in seconds on a laptop with 8B open models.

First, pass the secret through an LLM and record, for each token, the rank of the actual next token.

Then prompt the model to write on a chosen topic, and force it to pick tokens at those ranks.

The result reads normally on that topic and has the same token count as the secret.

With the same model and prompt, anyone can reverse the steps and recover the exact original.

These covers look natural to people, but models usually rate them less likely than the originals.

Quality is best when the model predicts the hidden text well, and worse for unusual domains or weaker models.

Security comes from the secret prompt and the exact model, and it gives the sender believable deniability.

One risk is hiding harmful answers inside safe replies for later extraction by a local model.