---
name: journal-arxiv-ai-attack-selection-in-20260609
description: >
  学术期刊学习笔记：arXiv AI - Attack Selection in Agentic AI Control Evaluations Meaningfully Decreases Safety
  蒙多每日期刊学习系统自动提取。
version: 1.0.0
author: mundo-journal-bot
priority: LOW
auto_activate: MANUAL
category: journal-learnings
source: arXiv AI
published: Mon, 08 Jun 2026 00:00:00 -0400
learned: 2026-06-09
---
# Attack Selection in Agentic AI Control Evaluations Meaningfully Decreases Safety

**来源**: [arXiv AI](https://arxiv.org/abs/2606.06529)
**学习日期**: 2026-06-09

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## 摘要

arXiv:2606.06529v1 Announce Type: new Abstract: An attacker that strategically chooses when to attack is much harder to catch than one that attacks indiscriminately. AI control is a safety framework for deploying capable but untrusted AI agents under the oversight of a weaker, trusted monitor and a limited human audit budget. Control evaluations stress-test these protocols by pitting a red-team attack policy against the blue-team monitor, but current evaluations typically assume attackers that d

## 关键发现

1. 06529v1 Announce Type: new Abstract: An attacker that strategically chooses when to attack is much harder to catch than one that attacks indiscriminately

## 蒙多笔记

> 蒙多从此文中学到：Attack Selection in Agentic AI Control Evaluations...
> 此知识已纳入蒙多的学术知识库。

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