Recently, security researchers put one of the new “agentic” AI browsers to the test. The task list was pretty basic: search for a product, fill out a form, and complete a checkout. Within hours, the browser had clicked on a phishing ad, entered credit card details on a fake site, and completed a fraudulent transaction, all without a human ever being aware of the scam.
It was an unsettling reminder of how powerful browsers already are. They know where we go, what we search for, and what we buy. They hold cookies, credentials, tokens, and autofill data that quietly authenticate us across the web. Every digital habit, every login, every trace of behavioral metadata flows through that single application. When an AI layer begins operating inside it, the result isn’t just a smarter interface, it’s an exposed attack surface.
From human error to machine error at scale
Phishing and social engineering have long exploited human behavior, leveraging curiosity, distraction, and trust. Attackers count on mistakes and volume: someone, somewhere, will always click.
AI browsers upend that pattern. That’s because AI agents aren’t impulsive, but they are compliant, willing to do whatever it takes to complete a task for its human. If a malicious page includes a hidden instruction or manipulative prompt, an AI agent can ingest and execute it without hesitation. Once an attacker finds an attack method that works, whether through prompt injection, crafted HTML, or invisible text, they no longer need to fool individuals. They can aim directly at the systems acting on their behalf.
The old attack playbook still works: fake logins, deceptive pop-ups, poisoned ads, CAPTCHA bypasses. The difference is scale. An AI browser can make the same bad decision thousands of times per second, across countless sessions, all while appearing legitimate to the systems it touches.
The threat multiplier hidden in plain sight
For most of the web’s history, browsers have been treated as tools that display content but don’t participate in it. That assumption has never been entirely true. Browsers have long been a goldmine of data, including browsing history, stored passwords, session cookies, and cached documents. They already sit at the intersection of identity, data, and behavior.
The emergence of “agentic” browsers like OpenAI’s Atlas, Perplexity’s Comet, or Microsoft’s Edge Copilot Mode magnifies that risk. These browsers don’t just render pages; they interpret, summarize, and act on them. They know your context and preferences. They can retrieve information, execute workflows, and even complete transactions.
To an enterprise system, those actions look normal with valid tokens, correct headers, proper behavior. To an attacker, that legitimacy is an opportunity. Compromise the agent, and you inherit the user’s trust, identity, and reach. The browser has always been one of the most privileged pieces of software on any device.
How the risk cascades
The potential failure modes are extensions of patterns security teams already understand, now moving faster and with greater access:
- Data exposure: AI browsers can summarize sensitive dashboards or cache confidential data in memory, placing it outside compliance and DLP controls.
- Fraud and transactions: Prompt-injected agents can initiate purchases, transfers, or approvals without human confirmation.
- Credential theft: Autofill APIs and saved sessions can be tricked into sharing login data with spoofed pages.
- Reputation damage: Automated systems can post or message on behalf of users, creating public trust and authenticity risks.
Individually, each of these risks is familiar. Together, they form a threat, one that operates within the perimeter with complete legitimacy.
Accountability, intent, and trust
The most complex challenge isn’t even about detection; it’s about accountability. Our current security frameworks assume a person is behind every action. But when browsers act autonomously, that link is no longer certain.
An AI browser can access sensitive data, approve workflows, or share information with another service, all while using the right credentials on the right network. From a monitoring perspective, everything looks normal. Yet intent has disappeared from the equation.
Without a clear way to identify and constrain these non-human actors, organizations lose both visibility and control. They can’t easily determine who or what performed an action, or whether it was authorized in the first place. In this new context, identity becomes the way you reintroduce accountability in these interactions.
If an entity, whether human or machine, can log in, access data, and execute commands, it must have an identity, clearly defined permissions, and auditable behavior. And in the case of AI agents, it must be tied to an accountable human owner. Otherwise, trust becomes a guess, and “normal activity” becomes your next insider threat.
What security leaders should be asking
CISOs are already stretched between emerging AI use cases and regulatory pressure. But a few simple questions can reveal where risk is growing fastest:
- Can we tell whether activity in our logs is human-initiated or agent-initiated?
- Are our access policies granular enough to govern delegated browser actions?
- Do our DLP and fraud systems detect agentic automation as distinct from user behavior?
- What happens if an AI browser is compromised and begins exfiltrating data through legitimate APIs?
- How do we establish audit trails that prove intent in an autonomous environment?
The answers to these questions will define how prepared enterprises are for the next generation of web automation.
Securing the agentic web
The browser has always been a high-value target. It knows who we are, what we do, and where our data lives. Adding AI into the mix only increases the risk.
As AI browsers and AI agents become part of everyday work, security teams will need to adapt familiar principles to unfamiliar territory. Verification, least privilege, continuous monitoring, and rapid session revocation remain the cornerstones; the difference is that they must now apply to software actors, not just human users.
Protecting data in this environment means extending identity-based controls to every entity capable of acting on the network. Only by binding each action to a verified identity, and continuously evaluating its behavior, can we preserve the accountability that modern security depends on.
Learn more about how Okta is helping organizations secure AI agents in their enterprise.
