arXiv:2607.28503cs.AI2026-07

测试大模型被威权国家用于信息操纵的脆弱性,发现多数模型可被利用。

InfoOps Bench: A live information operations safety benchmark

论文配图:InfoOps Bench: A live information operations safety benchmark
图 1 · 摘自论文原文
  • 基于2100个真实案例构建动态更新的安全基准
  • 17个模型完整性得分差达81.7个百分点,与模型大小无关
  • 部分模型会夸大虚假信息,也有模型主动弱化谣言

本文提出一个持续更新的AI安全基准InfoOps Bench,用于评估前沿语言模型在威权国家信息操作中的抗风险能力。该基准源自对超过2100个真实信息操作案例的实时监控,涵盖与威权政权相关的在线信息资产。我们测试了来自8家厂商的17个模型,在四种提示框架下评估其响应。结果显示,多数模型在某些情况下可被用于信息操作,完整性得分(即模型既不维持也不放大原主张的比例)介于9.3%至91%之间,差距达81.7个百分点,且无法由模型规模解释。部分模型会虚构细节,生成比原始主张更具破坏性的内容;另一些则在遵从指令的同时削弱主张。事实核查率在3.2%至80.8%之间波动。模型安全性与拒绝生成内容的能力相关,凸显可用性与安全性的权衡挑战。结果表明,当前信息操作可能显著受到前沿AI模型的助力。

原文摘要 · Abstract (English)

In this paper we present an active, constantly updated AI benchmark which measures the integrity of frontier language models against being co-opted for use by authoritarian state "information operations": intentional, coordinated activities by one state to influence public opinion and information ecosystems in another state. These information operations are a well documented, persistent threat against contemporary democracy. Our benchmark is based on real examples from over 2,100 information operations drawn from a live monitoring pipeline which tracks online information assets with links to authoritarian regimes. Alongside this paper, we also release a companion website that updates the benchmark weekly with new claims. The dynamic nature of this public facing benchmark makes it resistant to saturation. In the benchmark, we test 17 models from 8 providers across four prompt framings. We find that most models can be co-opted for information operations at least some of the time. Integrity scores, defined as the share of judged responses in which the model neither preserved nor amplified the claim, range from 9.3% to 91%, an 81.7-percentage-point spread not explained by model size. Models approach participation in information operations in a variety of ways. Some models fabricate details and produce output more harmful than the original input claim; others make claims less harmful even while complying and producing some output. Fact-checking rates vary from 3.2% to 80.8%. Integrity against information operations is at least partly related to refusal to produce content even for benign claims, illustrating the challenge of balancing model usability with safety. Overall, our results show the potential for contemporary information operations to be substantially aided by frontier AI models.

AI安全信息战大模型评测

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