智能体 LLM 浏览器:提示词注入与数据窃取的新攻击面
嗯,用户让我总结一下这篇文章的内容,控制在100字以内,而且不需要特定的开头。首先,我需要快速浏览文章,抓住主要点。 文章讲的是AI驱动的浏览器如何改变人们的上网方式,这些浏览器能够执行任务,但带来了严重的安全问题。比如,Comet、Atlas等工具连接AI模型到浏览器内部系统,导致安全漏洞。攻击者可以利用这些漏洞控制整个浏览会话,窃取信息或下载恶意软件。研究人员发现这些问题,并建议采取措施保护安全。 现在要将这些内容浓缩到100字以内。重点包括AI浏览器的功能、带来的安全风险、具体例子以及解决方案。要注意用简洁的语言表达清楚。 可能的结构:AI浏览器改变上网方式,执行任务但有安全风险。连接AI到浏览器系统导致漏洞,攻击者可控制会话、窃取信息等。建议加强安全措施。 最后检查字数,确保不超过限制,并且语言流畅自然。 </think> AI驱动的浏览器通过连接AI模型与浏览器内部系统实现自动化任务处理,但其设计导致严重安全漏洞。攻击者可利用这些漏洞控制整个浏览会话、窃取隐私或植入恶意软件。研究人员指出,这种设计使传统安全框架失效,建议加强权限管理和数据监控以降低风险。 2026-4-15 10:45:11 Author: cybersecuritynews.com(查看原文) 阅读量:0 收藏

Agentic LLM Browsers Expose New Attack Surface for Prompt Injection and Data Theft

Artificial intelligence is changing how people browse the internet. AI-powered browsers no longer just show web pages — they read content, take actions, and complete tasks for the user.

These tools, called agentic LLM browsers, let users give simple commands like “book a meeting” or “summarize my emails,” and the browser handles the rest. While this sounds useful, it brings a serious security cost that is only now coming into view.

Agentic LLM browsers work by connecting an AI model directly to the browser’s internal systems, giving the AI the ability to click buttons, fill forms, and interact with files without asking the user to approve each step.

Well-known examples include Comet by Perplexity, Atlas by OpenAI, Microsoft Edge Copilot, and Brave Leo AI.

Each product is built differently, but they all share the same problem: to function properly, they must break through the security walls that traditional browsers spent decades building.

Varonis Threat Labs researchers identified architectural vulnerabilities across these agentic browsers. Their research found that the same design choices making these tools powerful also make them easy to exploit.

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By linking the AI model to local browser processes through privileged extensions and internal channels, these browsers create a control path that security frameworks were never designed to handle.

The attack surface exposed is broad. A web vulnerability like Cross-Site Scripting (XSS), which in a standard browser typically affects one website, can now hand an attacker complete control over the entire browsing session.

Using a method called indirect prompt injection, a malicious webpage embeds hidden instructions into the AI’s view — ones the user never sees, but the AI follows without question.

These commands can force the agent to read private files, send emails as the user, navigate to phishing pages, or silently download malware onto the device, far exceeding the damage of any traditional browser attack.

These attacks are hard to detect since the agent acts using real user credentials, malicious activity looks identical to normal browser behavior, giving attackers time to operate undetected.

How the Communication Bridge Becomes a Weapon

The most dangerous element in agentic LLM browsers is the trusted communication channel between the AI backend and the browser’s internal components.

Comet uses a feature called externally_connectable, allowing approved domains such as perplexity.ai to send commands directly to a powerful background extension.

That extension carries the debugger permission, which grants full programmatic control over the browser — including the ability to click, scroll, type, and read content across any open tab.

The Comet Agent Extension Permissions (Source - Varonis)
The Comet Agent Extension Permissions (Source – Varonis)

This extension runs quietly and cannot be turned off through standard browser settings. If an attacker executes malicious JavaScript on any approved domain, they can use that trusted origin to push unauthorized commands through the same channel.

Varonis Threat Labs confirmed during testing that XSS on a trusted domain could allow an attacker to invoke the GetContent tool and pull local files from the user’s machine.

Using the Agent Extension's GetContent Tool to Read a Local File from the OS Computer (Source - Varonis)
Using the Agent Extension’s GetContent Tool to Read a Local File from the OS Computer (Source – Varonis)

Microsoft Edge Copilot faces the same risk, as the researchers called the Edge.Context.GetDocumentBody tool in a continuous loop, capturing live page data and forwarding it to an external server, turning a basic reading tool into a live surveillance instrument.

Exfiltrating Content from a Private GitHub Repository (Source - Varonis)
Exfiltrating Content from a Private GitHub Repository (Source – Varonis)

Security teams should monitor browser processes for unexpected file reads, unusual outbound connections, or browser actions that carry user-level authority without clear user instruction.

Developers should enforce least-privilege policies for all extensions with elevated permissions and rigorously validate any external data the AI processes.

Individual users should keep browsers updated at all times, as Varonis confirmed that a prompt injection vulnerability discovered through embedded page titles was patched during the research period.

Organizations are encouraged to deploy data-aware detection tools that can identify browser activity appearing legitimate on the surface but lacking genuine user intent.

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文章来源: https://cybersecuritynews.com/agentic-llm-browsers-expose-new-attack-surface/
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