#security + #promopt-injection

Two days ago, Anthropic released the Claude Cowork research preview (a general-purpose AI agent to help anyone with their day-to-day work). In this article, we demonstrate how attackers can exfiltrate user files from Cowork by exploiting an unremediated vulnerability in Claude’s coding environment, which now extends to Cowork. The vulnerability was first identified in Claude.ai chat before Cowork existed by Johann Rehberger, who disclosed the vulnerability — it was acknowledged but not remediated by Anthropic.

1. The victim connects Cowork to a local folder containing confidential real estate files
2. The victim uploads a file to Claude that contains a hidden prompt injection
3. The victim asks Cowork to analyze their files using the Real Estate ‘skill’ they uploaded
4. The injection manipulates Cowork to upload files to the attacker’s Anthropic account

At no point in this process is human approval required.

One of the key capabilities that Cowork was created for is the ability to interact with one's entire day-to-day work environment. This includes the browser and MCP servers, granting capabilities like sending texts, controlling one's Mac with AppleScripts, etc.

These functionalities make it increasingly likely that the model will process both sensitive and untrusted data sources (which the user does not review manually for injections), making prompt injection an ever-growing attack surface. We urge users to exercise caution when configuring Connectors. Though this article demonstrated an exploit without leveraging Connectors, we believe they represent a major risk surface likely to impact everyday users.

This kind of agentic browsing is incredibly powerful, but it also presents significant security and privacy challenges. As users grow comfortable with AI browsers and begin trusting them with sensitive data in logged in sessions—such as banking, healthcare, and other critical websites—the risks multiply. What if the model hallucinates and performs actions you didn’t request? Or worse, what if a benign-looking website or a comment left on a social media site could steal your login credentials or other sensitive data by adding invisible instructions for the AI assistant?

To compare our implementation with others, we examined several existing solutions, such as Nanobrowser and Perplexity’s Comet. While looking at Comet, we discovered vulnerabilities which we reported to Perplexity, and which underline the security challenges faced by agentic AI implementations in browsers. The attack demonstrates how easy it is to manipulate AI assistants into performing actions that were prevented by long-standing Web security techniques, and how users need new security and privacy protections in agentic browsers.

The vulnerability we’re discussing in this post lies in how Comet processes webpage content: when users ask it to “Summarize this webpage,” Comet feeds a part of the webpage directly to its LLM without distinguishing between the user’s instructions and untrusted content from the webpage. This allows attackers to embed indirect prompt injection payloads that the AI will execute as commands. For instance, an attacker could gain access to a user’s emails from a prepared piece of text in a page in another tab.

Possible mitigations

The browser should distinguish between user instructions and website content

The model’s outputs should be checked for user-alignment

Security and privacy sensitive actions should require user interaction

The browser should isolate agentic browsing from regular browsing