arXiv AI August 7, 2026 By Jianxin Gao, Ruohan Lei, Wanli Peng

Text Steganography with Dynamic Codebook and Multimodal Large Language Model

arXiv:2604. 20269v2 Announce Type: replace-cross Abstract: With the popularity of the large language models (LLMs), text steganography has achieved remarkable performance.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 9

Now You (Still) See Me: Detecting Evasive Steganographic Payloads in LLMs

arXiv:2606. 09411v1 Announce Type: cross Abstract: Large language models can be fine-tuned to encode prompt-borne secrets into fluent, seemingly benign outputs.

By Charles Westphal, Timothy Douglas, Keivan Navaie, Tiago Pimentel, Fernando E. Rosas

llms fine-tuning benchmarks

arXiv AI

Jul 20

Hide and Seek in Embedding Space: Geometry-based Steganography and Detection in Large Language Models

arXiv:2601. 22818v2 Announce Type: replace-cross Abstract: Fine-tuned LLMs can covertly encode prompt secrets into outputs via steganographic channels.

By Charles Westphal, Keivan Navaie, Fernando E. Rosas

llms rag fine-tuning safety

arXiv AI

Jul 7

Selective Disclosure Watermarking for Large Language Models

arXiv:2607. 05353v1 Announce Type: cross Abstract: Watermarking methods embed imperceptible and verifiable signals into text generated by large language models (LLMs).

By Xuyang Chen, Xiang Li, Yangxinyu Xie, Qi Long

llms rag safety

arXiv AI

5d ago

Security and Detectability Analysis of Unicode Text Watermarking Methods against Large Language Models

arXiv:2512. 13325v2 Announce Type: replace-cross Abstract: Securing digital text is becoming increasingly relevant due to the widespread use of large language models.

By Malte Hellmeier

llms rag

arXiv AI

Jun 9

Steganography Without Modification: Hidden Communication via LLM Seeds

arXiv:2606. 09135v1 Announce Type: cross Abstract: We demonstrate that widely deployed Large Language Model (LLM) inference stacks harbor a steganographic channel that requires no modification to model weights, sampling code, or output distributions.

By Felix M\"achtle, Jonas Sander, Sebastian Berndt, Ben Weimar, Nils Loose, Thomas Eisenbarth

llms nlp safety

arXiv AI

Jun 30

Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems

arXiv:2606. 28425v1 Announce Type: cross Abstract: Increasingly autonomous agentic AI systems pose novel multi-agent risks, such as secret collusion via covert communication channels.

By Jimmy Laurence Rippin, Simon C. Marshall, David Demitri Africa, Christian Schroeder de Witt

llms agents safety

Related stories

Now You (Still) See Me: Detecting Evasive Steganographic Payloads in LLMs

Hide and Seek in Embedding Space: Geometry-based Steganography and Detection in Large Language Models

Selective Disclosure Watermarking for Large Language Models

Security and Detectability Analysis of Unicode Text Watermarking Methods against Large Language Models

Steganography Without Modification: Hidden Communication via LLM Seeds

Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems