LLMs and Text-in-Text Steganography
Large Language Models have been shown to be highly effective at hiding secret messages within other text, posing new detection challenges.
Recent research suggests that Large Language Models (LLMs) possess a high degree of proficiency in performing text-in-text steganography. This technique involves hiding secret messages within seemingly innocuous text, making the hidden data difficult to detect through traditional analysis.
As noted by [Schneier on Security], the ability of LLMs to generate natural-sounding text while embedding hidden information poses new challenges for security professionals. This capability could potentially be exploited to facilitate covert communication channels or to exfiltrate data in ways that bypass standard content filters and monitoring tools.
Security researchers and organizations should be aware of this emerging threat vector as LLMs become more integrated into business and communication platforms. Further study is needed to develop detection methods capable of identifying steganographic content generated by AI models.