A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges
Comprehensive survey maps how LLMs have transformed linguistic steganography, cataloguing modern hiding techniques and detection countermeasures relevant to data exfiltration defence.
Summary written by editorial AI · Source link below
arXiv:2608.29077v1 Announce Type: new Abstract: Linguistic steganography hides secret messages in natural language text. Large language models (LLMs) have reshaped the field, but a systematic account of how these scattered advances collectively reshape the field in this new era is still missing. We provide one along four axes: 148 steganographic methods, 60 linguistic steganalysis countermeasures, 23 evaluation metrics, and 9 open challenges, each with taxonomies, reviews, and adoption analyses
Editorial Analysis
LLM-generated steganographic text could evade traditional DLP controls, requiring security teams to understand evolving covert communication techniques.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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