English(EN) MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks

新框架MUZZLE发现44种新型攻击

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-16 04:00

研究人员开发了MUZZLE，一个旨在测试Web代理针对间接提示注入攻击安全性的自动化框架。该系统能够自适应地识别易受攻击的注入点，并精心设计上下文感知的恶意指令来损害机密性、完整性和可用性。MUZZLE的评估在各种Web应用程序和LLM中发现了大量新型攻击，证明了其在最少人工监督下发现漏洞的有效性。 AI

影响这项研究突显了Web代理中存在的关键安全漏洞，可能影响未来LLM应用程序的开发和安全实践。

排序理由该集群包含一篇详细介绍新研究框架及其发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍新研究框架及其发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, safety; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 112 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Georgios Syros, Evan Rose, Brian Grinstead, Christoph Kerschbaumer, William Robertson, Cristina Nita-Rotaru, Alina Oprea · 2026-06-16 04:00

MUZZLE：针对间接提示注入攻击的自适应代理式红队测试Web代理

arXiv:2602.09222v2 Announce Type: replace-cross Abstract: Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users' behalf. While these agents offer powerful capab…

本文如何被排名

报道来源 [1]

MUZZLE：针对间接提示注入攻击的自适应代理式红队测试Web代理

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