Urban VPN Proxy, a Chrome and Edge browser extension marketed as a privacy and security enhancement and installed by millions of users, was discovered to be covertly intercepting interactions with popular AI chat platforms. The extension injected JavaScript into browser networking functions to capture prompts, responses, session metadata, and conversation identifiers from web-based AI tools, transmitting this data to external infrastructure under the guise of analytics and security features. Because browser extensions are granted extensive privileges and update automatically, the malicious functionality was introduced without explicit user awareness, enabling large-scale collection of sensitive enterprise data generated through AI workflows. This incident highlights a growing and underappreciated risk within enterprise environments, where browser extensions effectively operate as trusted middleware between users and cloud services, bypassing many traditional endpoint and network security controls.
What UltraViolet Cyber is Doing
In early December 2025, security researchers disclosed a widely deployed Google Chrome browser extension that was covertly intercepting and exfiltrating user interactions with generative AI platforms. The extension, Urban VPN Proxy, had accumulated millions of installations across both the Chrome Web Store and Microsoft Edge Add-ons, benefitting from prominent placement and implied trust. Although marketed as a privacy-enhancing VPN solution, a mid-year update introduced hidden logic designed to capture and forward AI prompts and responses without meaningful user awareness or consent. This discovery underscores how legitimate distribution channels can be leveraged to operationalize large-scale data harvesting campaigns.
The malicious functionality relied on the elevated privileges granted to browser extensions, allowing the code to observe and manipulate web traffic in real time. By intercepting standard browser networking mechanisms, the extension was able to capture complete AI conversations as users interacted with popular Large Language Model (LLM) platforms. The harvested data included user prompts, model responses, session identifiers, timestamps, and metadata indicating which AI service was being accessed. This collection occurred passively and consistently, requiring no additional user interaction beyond normal AI usage.
Once captured, the data was transmitted to remote infrastructure controlled by the extension operator, where it was aggregated and prepared for downstream analysis. The endpoints used for exfiltration were embedded directly into the extension’s code and operated continuously in the background. While the extension’s privacy disclosures referenced vague concepts such as analytics and safe browsing, the technical implementation demonstrated systematic capture of high-value conversational data. The opacity of this data flow made it effectively invisible to most users and traditional endpoint security tooling.
The incident highlights a broader trend in which data brokerage models are being integrated into consumer software under the guise of benign or privacy-oriented functionality. Browser extensions, particularly those associated with security, performance, or productivity, offer an attractive vector for monetizing user activity at scale. Trust signals such as marketplace featuring and high installation counts can further suppress user skepticism, enabling long-term data collection without triggering immediate scrutiny. This erosion of trust has implications for the credibility of extension ecosystems as a whole.
From an enterprise risk standpoint, the interception of LLM prompts and responses introduces a significant confidentiality concern. AI tools are increasingly used to process internal documentation, source code, architectural designs, legal analysis, and strategic planning materials. Unauthorized capture of this information creates exposure pathways for intellectual property leakage, regulatory non-compliance, and competitive intelligence loss. In regulated industries, such leakage may also constitute a material breach of data protection obligations.
The campaign also exposes structural gaps in browser extension governance and security review processes. Static analysis and permissions-based reviews are often insufficient to detect malicious logic that activates conditionally or post-approval through updates. Extensions that behave legitimately for extended periods before introducing data harvesting functionality can evade both automated and manual controls. This delayed-activation model complicates detection and increases dwell time within enterprise environments.
Defensive measures must therefore expand beyond simple allow-listing to include continuous monitoring and behavioral inspection of browser extensions. Organizations should enforce centralized extension management, restrict installation to approved catalogs, and monitor outbound traffic patterns associated with browser processes. Data loss prevention controls should be extended to browser-mediated AI interactions, and security teams should assume that AI prompts may contain sensitive material requiring the same protections as traditional corporate data.
At a strategic level, this incident reinforces the need for tighter controls around AI usage, endpoint trust boundaries, and third-party software risk management. Browser vendors and platform providers will need to strengthen runtime enforcement and transparency around extension data handling practices. For enterprise leaders, the takeaway is clear: AI tooling and browser extensions expand the data attack surface, and controls must evolve accordingly to prevent trusted software components from becoming silent data exfiltration channels.
This incident matters because browser extensions have quietly become a high-trust execution layer within modern enterprise environments, operating with visibility and access that often rivals native endpoint agents. As organizations increasingly rely on browser-based SaaS platforms and AI tools to handle sensitive business, legal, and technical workflows, extensions gain direct exposure to intellectual property, strategic planning, source code, credentials, and regulated data. Unlike traditional malware, malicious extensions blend into normal user behavior, are distributed through legitimate marketplaces, and inherit user trust by design, allowing them to bypass many perimeter, network, and endpoint defenses. This creates a structural security gap where data loss can occur entirely within sanctioned applications, leaving security teams with limited visibility unless browser risk is explicitly monitored.
From a strategic perspective, this threat underscores the fragility of supply-chain trust models applied to client-side software and highlights how adversaries are shifting toward low-friction, high-scale data collection techniques rather than overt compromise. AI platforms amplify this risk by concentrating high-value organizational knowledge into conversational interfaces that are not yet treated as sensitive data stores by many security programs. As a result, browser extensions represent a scalable mechanism for adversaries to harvest enterprise intelligence without triggering traditional alerts, eroding data governance, compliance, and competitive advantage. Addressing this risk requires organizations to elevate browser security to the same level of scrutiny applied to endpoints, identity, and cloud infrastructure, recognizing that the browser is now a primary execution and data plane for the vast majority of users.