Introduction
The digital landscape is evolving at a pace that often outstrips the regulatory and security frameworks designed to protect it. One of the most recent and unsettling developments is the emergence of AI‑powered web browsers such as Fellou and Comet, products of Perplexity AI. These browsers promise a more intuitive, conversational browsing experience by embedding natural‑language processing directly into the user interface. Instead of merely rendering HTML, they read, summarise, and even answer questions about the content of a web page in real time. While the convenience of having a built‑in AI assistant is undeniable, the integration of sophisticated machine learning models into the core of a browser introduces a new vector of attack that has been largely overlooked by both developers and security professionals.
The core issue is that AI browsers are not just tools for convenience; they are also potential conduits for malicious code. Because they process every piece of data that passes through them, they become prime targets for what researchers are calling “shadow AI malware.” This form of malware exploits the very AI capabilities that make the browsers attractive, hiding malicious payloads within seemingly innocuous AI interactions. The result is a stealthy threat that can bypass traditional security checks, infiltrate corporate networks, and compromise sensitive data without triggering conventional alarms.
In this post we will unpack how AI browsers work, why they are vulnerable, and what practical steps organizations can take to mitigate the risks. By understanding the mechanics of these threats, IT teams can better prepare defenses that extend beyond standard antivirus and firewall solutions.
Main Content
The Rise of AI Browsers
AI browsers represent a paradigm shift in how users interact with the web. Traditional browsers rely on static rendering engines and rely on external extensions for added functionality. In contrast, AI browsers embed a language model that can interpret the semantic content of a page, generate summaries, answer queries, and even suggest related resources. Fellou and Comet, for instance, claim to provide instant context, reducing the need to open multiple tabs or perform manual searches. For enterprises, the promise is clear: increased productivity, faster decision‑making, and a more engaging user experience.
However, the very features that make these browsers appealing also create a complex attack surface. The AI model must ingest raw HTML, JavaScript, and other resources, which can be manipulated by an attacker to deliver malicious code under the guise of legitimate content. Because the AI processes this data before it reaches the user, any malicious payload can be hidden within the AI’s output or within the data it consumes.
How AI Browsers Work
At a high level, an AI browser functions as a two‑tier system. The first tier is the traditional rendering engine that displays web pages. The second tier is the AI layer, which intercepts the content, runs it through a transformer‑based model, and produces natural‑language responses or summaries. The AI layer also handles user queries, translating them into API calls that fetch relevant information from the web.
This architecture means that every request and response passes through the AI model. If an attacker can inject code that is interpreted by the model, they can effectively hijack the browser’s behavior. For example, a malicious script could be disguised as a data payload that the AI model treats as a normal piece of information. When the model processes it, the script could be executed in the browser context, allowing the attacker to exfiltrate data or install additional malware.
Security Vulnerabilities
The integration of AI into browsers introduces several new vulnerabilities:
- Model Misinterpretation – AI models can misinterpret malicious inputs as benign, especially if the input is crafted to mimic legitimate data. This misinterpretation can lead to the execution of hidden code.
- Data Leakage – Because AI models process sensitive data, there is a risk that the model’s internal state or logs could inadvertently expose confidential information.
- Supply‑Chain Attacks – The AI model itself may be compromised if the training data or the model weights are tampered with. An attacker could insert malicious logic into the model that activates under specific conditions.
- Shadow AI Malware – This is a new class of malware that leverages the AI layer to conceal malicious payloads. By embedding malicious code within the AI’s input or output, attackers can bypass traditional signature‑based detection.
Shadow AI malware is particularly insidious because it can remain dormant until the AI processes a specific trigger. Traditional security tools that scan for known malware signatures may not detect it because the malicious code is not present in a conventional binary form.
Real‑World Implications
The implications for corporate environments are profound. A single compromised AI browser can serve as a foothold for lateral movement within a network. Because the browser is often granted extensive permissions—access to local files, network resources, and user credentials—an attacker can use it to pivot to other systems. Moreover, the AI’s ability to generate convincing text can be exploited to craft phishing messages that appear legitimate, further eroding trust in internal communications.
In a recent incident reported by AI News, a mid‑size consulting firm discovered that an employee’s AI browser had silently installed a keylogger after visiting a seemingly innocuous website. The keylogger was delivered via the AI layer, which had been tricked into executing a hidden script. The breach went unnoticed for weeks, during which sensitive client data was exfiltrated.
Mitigation Strategies
Mitigating the risks posed by AI browsers requires a multi‑layered approach:
- Strict Application Whitelisting – Only allow approved browsers and extensions on corporate devices. AI browsers should be evaluated against security criteria before deployment.
- AI Model Auditing – Conduct regular audits of the AI models used in browsers. Verify that the training data is clean and that the model’s outputs are consistent with expected behavior.
- Runtime Monitoring – Deploy endpoint detection and response (EDR) solutions that can detect anomalous behavior originating from browsers, such as unexpected network connections or file modifications.
- User Education – Train employees to recognize suspicious AI responses and to verify information through trusted sources before acting on it.
- Zero‑Trust Architecture – Implement network segmentation and least‑privilege access controls so that even if a browser is compromised, the attacker’s reach is limited.
By combining these measures, organizations can reduce the attack surface and ensure that the benefits of AI browsers do not come at the cost of security.
Future Outlook
The trend toward AI‑enhanced browsers is unlikely to slow down. As language models become more powerful and accessible, we can expect a proliferation of browsers that offer deeper integration with AI services. The challenge for security professionals will be to keep pace with the evolving threat landscape. Future research should focus on developing AI‑specific security frameworks that can detect and mitigate shadow AI malware before it reaches the user. Additionally, industry standards for AI model transparency and accountability will be essential in building trust in these emerging tools.
Conclusion
AI browsers like Fellou and Comet promise a new era of web interaction, but they also introduce a sophisticated security threat that traditional defenses are ill‑prepared to handle. Shadow AI malware exploits the very capabilities that make these browsers useful, hiding malicious code within AI inputs and outputs. Organizations that adopt these tools must do so with a clear understanding of the risks and a robust mitigation strategy in place. By combining strict application controls, AI model audits, runtime monitoring, and user education, businesses can harness the productivity gains of AI browsers while safeguarding their data and infrastructure.
Call to Action
If your organization is considering deploying AI‑powered browsers, start by conducting a comprehensive risk assessment that includes an evaluation of the AI models and their training data. Implement strict whitelisting policies and ensure that endpoint security solutions are tuned to detect anomalous browser behavior. Engage with vendors to understand how they secure their AI layers and request transparency reports. Finally, invest in ongoing training for your IT and security teams so they can stay ahead of emerging threats. By taking these proactive steps, you can enjoy the benefits of AI browsing without compromising the security of your enterprise.
