This research introduces a novel LLM-based steganography framework that conceals secret messages within natural-looking text while maximizing efficiency and security.

• Formulates text steganography as a constrained Markov Decision Process to optimize embedding efficiency
• Achieves higher bit-per-token ratio while maintaining the natural appearance of generated text
• Enhances security against statistical detection methods that analyze text patterns
• Demonstrates practical applications for covert communication and content watermarking

This advancement matters for security professionals by providing more robust methods for information hiding while addressing the increasing sophistication of detection algorithms in digital forensics.

Original Paper: Relatively-Secure LLM-Based Steganography via Constrained Markov Decision Processes