Steganography in Large Language Models

Researchers have developed a method to hide secret data inside large language models (AI systems trained on massive amounts of text) by encoding information into the model's parameters during training. The hidden data doesn't interfere with the model's normal functions like text classification or generation, but authorized users with a secret key can extract the concealed information, enabling covert communication. The method leverages transformers (the neural network architecture behind modern AI language models) and its self-attention mechanisms (components that help the model focus on relevant parts of input) to achieve high capacity for hidden data while remaining undetectable.