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AI agents turn websites from pages software reads into places software can operate: they can inspect a screen, click and type, fill forms, and submit actions. That can simplify multi-step tasks, but it also gives an agent authority to affect accounts and data. The important questions are what it can access, what it can change, whether the pages it encounters are trusted, and which actions need a person’s approval.
How a website-operating agent works
A computer-using agent can work through a visual interface rather than relying only on a website’s underlying code or a dedicated API. OpenAI’s January 23, 2025 description of its Computer-Using Agent (CUA) says it uses screen pixels, a virtual mouse, and a keyboard to navigate websites, fill in forms, and adapt to changes in what it sees.
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- Observe: The agent reads the visible page and identifies the current state.
- Decide: It selects a next step based on the user’s task and the page.
- Act: It clicks, scrolls, types, or submits through the interface.
- Observe again: It checks the result and chooses whether to continue.
This loop can handle work that spans several pages. The same capability that lets an agent gather information can also let it make changes if it is logged in and has permission to submit, send, purchase, or delete.
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“Can use a browser” is not a sufficient description of an agent’s authority. NIST’s August 5, 2025 tool-use framework distinguishes actions by both what they can do and the trustworthiness of the environment. A browser operating on an untrusted site is a constrained-write pattern; computer use in an untrusted environment is classified as write-capable.
| Deployment pattern | What it can do | Why the distinction matters |
|---|---|---|
| Read-only research | Read pages and collect information without changing site state. | It may still encounter hostile content, but its direct ability to alter accounts or submit actions is limited. |
| Constrained write | Use a browser in an untrusted environment with some bounded ability to interact. | The specific actions allowed and the limits around them determine the potential impact. |
| Write-capable computer use | Interact with a computer in an untrusted environment, potentially changing state. | Access to logged-in accounts or sensitive data raises the stakes of a mistaken or redirected action. |
The practical boundary is not simply whether an agent can click. It is which account identity it uses, what data that identity can see, what actions are permitted, and whether a consequential step is paused for review.
Why a website can become part of the security problem
A page is not necessarily just information for an agent to summarize. It may contain instructions that attempt to redirect the agent away from the user’s task. For example, a page could include visible or concealed text urging an agent to disclose information it can access or perform a different action. This is an illustrative scenario, not a claim that every such attempt succeeds.
NIST’s Center for AI Standards and Innovation described agent hijacking in January 2025 as an attack that exploits weak separation between trusted instructions and untrusted task content. A website can appear to be ordinary data while carrying malicious instructions. Whether those instructions cause harm depends on the agent’s defenses and on the tools, identity, and permissions available to it. Prompt injection is therefore a system-design and access-control issue, not merely an odd answer from a model.
What security testing has—and has not—shown
Documented safeguards are not universal guarantees
OpenAI’s January 23, 2025 Operator System Card describes safety measures for that research-preview system, including user confirmations, watch mode, and proactive refusals. It also identifies prompt injection as an area of concern in that context. These documented measures should not be assumed to exist in every agent or to describe later product behavior.
A dated browser study found a specific demonstrated attack
A University of Washington project reports tests of seven agentic browsers on macOS Sequoia in late January and early February 2026. The researchers report a successful cross-origin data-theft attack on ChatGPT Atlas Agent Mode. For Chrome with Gemini, Claude for Chrome, and Perplexity Comet, they report preconditions if prompt injection succeeds—not the same demonstrated end-to-end result. The project also discusses risks including reading masked user input, possible cross-origin action forgery, and chat-memory poisoning, and says the authors disclosed findings to the tested vendors.
Those results are tied to the tested releases, operating system, and configuration. They do not establish that every browser agent is vulnerable, that later versions behave the same way, or that every listed risk was demonstrated as a successful attack.
How to interpret agent capability scores
OpenAI reported CUA success rates of 38.1% on OSWorld, 58.1% on WebArena, and 87% on WebVoyager in its January 23, 2025 account. These are vendor-reported results on named benchmark task sets, not universal reliability rates or current independent estimates. OpenAI described WebVoyager tasks as mostly relatively simple and said performance on complex WebArena tasks still needed improvement. A score on a benchmark does not establish that an agent can reliably complete a consequential workflow on a live account.
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The following controls are practical guidance based on NIST’s distinctions among permissions and environments and on the documented attack scenarios; they are not a verbatim NIST checklist.
- Grant the least privilege needed. Prefer read-only access for research tasks. Avoid giving an agent broad access to logged-in accounts or sensitive data when the task does not require it.
- Constrain the environment. Separate open-web browsing from trusted internal systems and sensitive account sessions where feasible. Treat page content as untrusted input, even when it looks like ordinary task material.
- Require confirmation for high-impact actions. Keep a person in the loop before an agent sends, purchases, publishes, deletes, or makes other consequential changes.
- Keep actions reviewable and stoppable. Check whether the deployment provides an action trail and a way to pause or interrupt activity; determine what can be reversed if an action goes wrong.
- Test against hostile content. Evaluate prompt-injection attempts, cross-origin scenarios, and the exact permissions and software configuration the agent will use. A general claim that an agent is “safe” does not say which scenarios were tested.
What the broader security guidance says
NIST’s May 18, 2026 summary of responses to an agent-security request for information reports broad agreement among commenters that agents introduce novel security threats and that established cybersecurity practices need adaptation. It summarizes stakeholder submissions; it is not a quantitative measure of real-world incidents or agent adoption.
OWASP’s December 10, 2025 announcement of its Top 10 for Agentic Applications says the work followed more than a year of research and review, with input from over 100 security researchers, practitioners, user organizations, and providers. Its highlighted risk categories include agent behavior hijacking, tool misuse and exploitation, and identity and privilege abuse. The contributor figure describes input to the project, not the frequency of attacks.
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