大模型能藏信息在文本里，可能被用来欺骗检测系统。
The Steganographic Potentials of Language Models
- 用强化学习训练模型，让其学会在正常文本中暗藏信息。
- 未微调模型也有基础隐藏能力，但微调后隐蔽性大幅提升。
- 适合关注AI安全、对抗攻击或隐写术研究者阅读。
大型语言模型（LLMs）将信息隐藏于普通文本中的潜力，对检测和防范不一致的AI代理构成挑战，并损害了模型推理的可信度。我们研究了通过强化学习（RL）微调的LLM在三类场景下的隐写能力：(1) 开发隐蔽编码方案，(2) 在提示下执行隐写操作，(3) 在真实场景中于未被提示时隐藏推理过程。实验与非微调行为评估结果表明，当前模型虽具备基础的隐写安全性和容量，但明确的算法指导显著提升了其信息隐藏能力。
原文摘要 · Abstract (English)
The potential for large language models (LLMs) to hide messages within plain text (steganography) poses a challenge to detection and thwarting of unaligned AI agents, and undermines faithfulness of LLMs reasoning. We explore the steganographic capabilities of LLMs fine-tuned via reinforcement learning (RL) to: (1) develop covert encoding schemes, (2) engage in steganography when prompted, and (3) utilize steganography in realistic scenarios where hidden reasoning is likely, but not prompted. In these scenarios, we detect the intention of LLMs to hide their reasoning as well as their steganography performance. Our findings in the fine-tuning experiments as well as in behavioral non fine-tuning evaluations reveal that while current models exhibit rudimentary steganographic abilities in terms of security and capacity, explicit algorithmic guidance markedly enhances their capacity for information concealment.
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