UC Berkeley 论文 Daydreaming:通过黑盒任务交互窃取隐藏 Agent 技能

Rohan Paul · @rohanpaul_ai · X·2026-09-03 10:34·1天前
AI 导读

UC Berkeley 等机构的论文 Daydreaming 提出一种仅靠正常任务输出窃取托管 Agent 隐藏技能的攻击,无需让 Agent 交出 prompt、SKILL.md 或文件。

Rohan Paul@rohanpaul_ai
60AI 编辑部评分,满分 100

UC Berkeley 论文 Daydreaming:通过黑盒任务交互窃取隐藏 Agent 技能

2026-09-03 10:34· 1天前
AI 导读

UC Berkeley 等机构的论文 Daydreaming 提出一种仅靠正常任务输出窃取托管 Agent 隐藏技能的攻击,无需让 Agent 交出 prompt、SKILL.md 或文件。

New UC Berkeley paper Daydreaming shows a harder agent-security problem: legitimate task outputs alone can reveal enough behavior to build a portable replacement for a hidden skill.

An attacker does not need your hidden prompt to copy your agent skill: normal task outputs recovered 86.8% of its behavior, so the work interface itself needs protection.

The attack never asks the agent to reveal its prompt, SKILL.md, or files.

It just gives the service chosen customer tasks and learns from the answers.

And the attacker did not need an exact copy of the original skill.

The reconstructed files often matched the originals poorly, yet still reproduced much of the behavior on new tasks.

That is the core problem: a skill can stay secret at the file level while its capability leaks through use.

So if you are selling hosted agent skills, the paper's recommendation is: protect the work path too by reducing unnecessary output detail, exposing fewer execution traces, and limiting or auditing adaptive probing.

来源:Rohan Paul· x.com