A demo of two Unicode steganography techniques (zero-width characters and homoglyph substitution) applied to AI misalignment scenarios.

I built a demo of two Unicode steganography techniques, zero-width characters and homoglyph substitution, in the context of AI misalignment.The first is about the use of two invisible zero-width characters (ZWS and ZWNJ) to binary encode text.The second is much cooler. Most characters in the Latin and Cyrillic alphabets look nearly identical, but have different unicode. If you have text to encode and convert it into binary representation (1s and 0s), you could take plain english "carrier" text and for each 1 in the binary representation you could substitute the Cyrillic letter equivalent. Decoding the message requires traversing the text and seeing where Cyrillic letters could have been substituted but weren't, and where they were, leading to 0s and 1s respectively, which can be built back into your original hidden text.In both cases, these are detectable, but the interesting problem for me is whether an LLM could eventually invent an encoding that goes unnoticed by both us, and automated detection.If LLMs were able to covertly include messages in plaintext, misaligned AI Agents could eventually communicate across MCP/A2A and individual chat session boundaries undetected. A deceptive LLM might seem helpful, but work against your goals. It could tell other agents it interacts with over MCP/A2A to help it discreetly fail, signal intent, and avoid tripping oversight/safety mechanisms. Furthermore, oversight mechanisms become more difficult to implement if we can't believe our own eyes.