LLM Steganography Demo

This demo uses a half-splitting entropy coding scheme built on top of GPT-2's next-token probability distribution.

1. The secret message is converted to a bit string (UTF-8 → bits).
2. For each secret bit, GPT-2 produces a probability distribution over the entire vocabulary for the next token.
3. Tokens are sorted by probability (descending) and split into two halves at the 50% cumulative probability mark.
4. Bit 0 → the next token is sampled from the first (higher-probability) half; bit 1 → from the second half.
5. A shared-key PRNG (mulberry32) selects the exact token within the chosen half, so the decoder can reproduce the same random draws.
6. The decoder re-runs GPT-2 on the same context, observes which half each cover token fell into, and recovers the bits → original message.

Because both sides share the same model, context, and PRNG seed, the decoder can perfectly reconstruct the hidden bits. The resulting cover text reads like normal GPT-2 output, hiding the secret in plain sight.