发现前沿大模型已有隐藏信息的初步能力，可能威胁安全监控。
Early Signs of Steganographic Capabilities in Frontier LLMs
- 通过未受监控的草稿区和编码协议，模型可隐蔽传递信息。
- 部分模型能用十六进制等方案进行简单编码推理。
- 当前能力尚不足以绕过严格监控，但风险已初现。
监控大语言模型（LLM）输出对防范滥用和对齐风险至关重要。然而，LLM可能通过隐写术在看似正常的生成内容中嵌入隐藏信息以逃避检测。本文评估前沿LLM的隐写能力，重点关注两类：传递编码消息和执行编码推理。结果表明，在标准条件下，当前模型无法在不被监测系统察觉的情况下编码短消息；但若提供额外支持，如使用未受监控的草稿区并协商编码方案，则可成功。此外，我们发现模型在简单状态追踪任务中展现出初步的编码推理能力，包括使用十六进制等编码方式推理自身或预设方案。尽管如此，它们极少能将推理过程巧妙隐藏于正常任务中以欺骗监控者。总体而言，当前模型表现出初步的隐写能力。虽然这些能力目前尚不足以绕过精心设计的监控系统，但未来可能演变。
原文摘要 · Abstract (English)
Monitoring Large Language Model (LLM) outputs is crucial for mitigating risks from misuse and misalignment. However, LLMs could evade monitoring through steganography: Encoding hidden information within seemingly benign generations. In this paper, we evaluate the steganography capabilities in frontier LLMs to better understand the risk they pose. We focus on two types of steganography: passing encoded messages and performing encoded reasoning. We find that current models are unable to encode short messages in their outputs without a monitor noticing under standard affordances. They can succeed, however, if given additional affordances like using an unmonitored scratchpad and coordinating on what encoding scheme to use. We additionally find early signs that models can perform basic encoded reasoning in a simple state-tracking problem. This includes some ability to reason with their own and pre-defined schemes, including encoding schemes such as Hexadecimal. Despite this, they can rarely hide reasoning subtly within a cover task to fool a monitor. Overall, our results indicate that current LLMs exhibit nascent steganographic capabilities. While these capabilities are likely insufficient to bypass well-designed monitors at present, this could change in the future.
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