LLMs Can Hide Text in Other Text of the Same Length

A meaningful text can be hidden inside another, completely different yet still coherent and plausible, text of the same length. For example, a tweet that celebrates a political leader could hide a tweet containing a harsh critique against the same leader, or an ordinary product review could conceal a secret manuscript. This uncanny possibility is now within reach thanks to Large Language Models; in this paper we present Calgacus, a simple and efficient protocol to achieve it. We show that even modest 8‑billion‑parameter open‑source LLMs are sufficient to obtain high‑quality results, and a message as long as this abstract can be encoded and decoded locally on a laptop in seconds. The existence of such a protocol demonstrates a radical decoupling of text from authorial intent, further eroding trust in written communication, already shaken by the rise of LLM chatbots. We illustrate this with a concrete scenario: a company could covertly deploy an unfiltered LLM by encoding its answers within the compliant responses of a safe model. This possibility raises urgent questions for AI safety and challenges our understanding of what it means for a Large Language Model to know something.