Comparison · RedactSure Research
Chris Sowa · Founder, RedactSure
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RedactSure vs Browser Use: 2026 Comparison & Alternative
Browser Use substitutes placeholders at typing time, while RedactSure, a Browser Use alternative built for AI agents, substitutes values on the rendered screen. RedactSure is an alternative way to think about agent browsing security, because it protects sensitive data as it is rendered on a screen, before an agent uses it.
Browser Use's own page states "Real values are injected directly into form fields after the LLM call," which is where a Browser Use alternative has to place its control differently: the library substitutes a placeholder in the text an agent types, and RedactSure substitutes a value on the page the model reads. Browser Use protects the input to a form, and it restricts the domains the agent may visit (Browser Use, 2026). RedactSure protects the screen itself, so the account number a billing portal displays becomes ACCT_001 before any model sees it.
One record type runs through the page: a customer account page in a billing portal the organization bought, showing the customer's name, address, bank account on file, balance and invoice history. The task is to change the autopay account and issue a credit. What Is a Protected Browser for AI Agents? describes the screen-level design this page measures against.
Key findings
- As a Browser Use alternative for agent work, RedactSure replaces the values on a rendered screen with consistent tokens such as ACCT_001 before any model reads it, and releases a real value only at the approved destination (What Is Render-Layer Tokenization?).
- Data a model reads from a screen is an exposure in its own right: injections that ask an agent to leak personal data it observed during a task reached "average attack success rates (ASR) around 20 percent" (Alizadeh et al., 2025).
- The Browser Use sensitive data page covers the text an agent types and not the text a page displays; its example is a login form, and the page does not describe what the model receives from the account page that renders after login (Can a Browser Agent Read an API Key It Just Generated?).
- Browser Use's page states that "The LLM only sees placeholders (x_user, x_pass), we filter your sensitive data from the input text," which keeps a typed credential out of the prompt (Browser Use, 2026).
- The same page says that "Real values are injected directly into form fields after the LLM call," and it offers two narrower controls: "Set use_vision=False to prevent screenshot leaks" and "Use Browser(allowed_domains=[...]) to restrict navigation" (Browser Use, 2026).
How do the two compare side by side?
The table sets Browser Use's own wording beside what a governed environment does on the same point, and names the check a buyer can run on one customer account page.
| Dimension | Browser Use, per its pages | RedactSure | What to verify |
|---|---|---|---|
| Where the control sits | "we filter your sensitive data from the input text" (Browser Use, 2026) | On the rendered page, before any model reads it | Whether the value on screen, or only the value typed, is covered |
| How coverage starts | "Use Browser(allowed_domains=[...]) to restrict navigation" (Browser Use, 2026) | The agent works inside the governed environment; the billing portal is unchanged | How many of the workflow's applications are covered on day one |
| What the model receives | "The LLM only sees placeholders (x_user, x_pass)" (Browser Use, 2026) | The screen with tokens such as ACCT_001 and ADDR_001, with the balance, which the autopay change does not use, as BAL_001, consistent for the run | One screen, shown field by field as the model read it |
| How real values are restored | "Real values are injected directly into form fields after the LLM call" (Browser Use, 2026) | Released at the approved destination when the named controller approves the change | Who releases the value, and what records the release |
| Who approves an action | The person running the agent; the page describes substitution, not an approval step (Browser Use, 2026) | A named person approves every payment, submission and record change | A person's name against the action, not a setting |
| The audit record | The run the person supervises; the page describes no export of what the model read | Every screen as tokens, every action and approval in the control record, exported to the SIEM | Whether the record shows what the model read |
Taken together, the two middle columns show that the products govern different objects, and each answer is complete only inside its own boundary. Browser Use governs the text the agent sends and the domains it may reach, so its answer is complete for a value typed into a form and quiet about the account number the portal displays. RedactSure governs the screen the model reads and who releases a token.
What does Browser Use protect, and where does it act?
Browser Use is an open-source browser automation library for AI agents, and its sensitive data page sets out a careful design for one kind of value: "The LLM only sees placeholders (x_user, x_pass), we filter your sensitive data from the input text," and "Real values are injected directly into form fields after the LLM call" (Browser Use, 2026). Two controls sit around the run: "Set use_vision=False to prevent screenshot leaks" and "Use Browser(allowed_domains=[...]) to restrict navigation" (Browser Use, 2026). For a login or a checkout, the model composes the action from a placeholder and the library puts the real value into the field after the call.
Every benefit depends on one condition: the value has to enter the run through the text the agent types. The page describes substitution in the prompt and in the input field, and it describes nothing for the values an application renders, such as an account on file, a balance or a one-time code shown after login. The allowlist narrows where the agent may go, and the vision setting removes screenshots, and neither changes the text a permitted page carries. A placeholder can carry a whole run when the value is only ever typed, which is what Can an AI Agent Work With a Placeholder Instead of a Real Credential? sets out. Of the 15 AI agents reviewed, 9 keep passwords or credentials away from the model and 0 do the same for the customer data a page shows, and Browser Use is the one whose placeholders cover login credentials only, on documentation read October 22 to 27, 2026, not a test (Do AI Agents Ask Before Making a Payment?).
What does an agent read when it opens a customer billing portal?
Consider the customer account page. The agent opens the billing portal under the controller's session, and the page renders the customer's name, billing address, bank account on file, balance and invoice list. Browser Use's control applies to the fields the agent fills, so a new autopay account can be held as a placeholder and injected after the model acts (Browser Use, 2026). The values the page displays are not part of that exchange: they arrive as text and stay in the model's context for the rest of the run.
The exposure is the model's context rather than the portal's database. Data minimisation asks for personal data "adequate, relevant and limited to what is necessary in relation to the purposes" of the processing (GDPR, 2016), and an autopay change needs an account, an amount and an approval, not the address and balance in a prompt. Once a value is in that context, browser boundaries do not contain it: a study of an agentic browser found that once an agent follows injected instructions in a logged-in browser, "same-origin policy (SOP) or cross-origin resource sharing (CORS) are all effectively useless" (Chaikin and Sahib, 2025).
There is an identity gap as well: the portal's access model is built around employees. Chase tells a business customer to "Assign your employees varying levels of access," and it offers "Require a second approver on transactions with Dual Control" (Chase, 2025). An agent that borrows the session sits inside that access, and the portal logs the person, not the agent. That is the gap What Is a Tethered Agent? sets out.
Which control fits which work?
Keep Browser Use for
- Login and checkout flows where the sensitive value is typed, since "The LLM only sees placeholders (x_user, x_pass)" until the field is filled (Browser Use, 2026).
- Runs confined to known sites, where "Use Browser(allowed_domains=[...]) to restrict navigation" keeps the agent on approved domains (Browser Use, 2026).
- Work where screenshots add risk without adding much, since "Set use_vision=False to prevent screenshot leaks" removes the image path (Browser Use, 2026).
Add RedactSure for
- Agents reading screens in applications the organization bought, where the model receives ACCT_001 instead of the bank account on file (What Is Render-Layer Tokenization?).
- Every payment, submission or record change the agent puts forward waits for a named person's approval (What Is Supervised Delegation?).
- The record of what the model received on each screen, exported to the customer's SIEM (What Is an AI Control Record?).
An organization running agents usually needs both, and the work above shows which protects the screen.
How do you test it on one workflow?
Run both products on one real workflow for an afternoon, with the vendors present and every answer written down.
Step 1. Choose one screen from the workflow the agent will run, such as the customer account page in the billing portal, rather than a data-flow diagram.
Step 2. Ask each vendor to replay the model's input for that screen, field by field: which fields arrived as values, which as tokens, and which never appeared at all.
Step 3. Write down every application in the workflow and mark whether that vendor's control covers it. The uncovered ones are the exposure on the first day, so count them before either vendor answers.
Step 4. Trace the bank account on file back to what released it, when, and whose name is in the record. A session log that names the controller answers a different question from a record of what the model read, and the second is the one an assessor will ask for.
Step 5. Calculate first-day coverage. If the workflow touches five applications and two are covered, coverage is two of five. Compare the costs that differ: the work to bring an application under control, and the tasks held until it is, so both figures sit beside the coverage count and are read together.
What does a governed environment change?
A governed environment is a control class rather than a product feature. For each task the planner proposes what to tokenize, starting from personal information, account numbers, credentials and record numbers.The agent works under its own session, recorded under its own identity, so the billing portal sees an agent rather than the controller. Values are replaced with consistent tokens before the model reads anything: the bank account appears as ACCT_001, the address as ADDR_001, and the balance, which the autopay change does not use, as BAL_001. Real values resolve only at approved destinations at the moment of an approved action, and a named person approves every payment, submission and record change. Every screen, action and approval is written to a control record that exports to the organization's SIEM.
The law already asks for a person in the loop. High-risk systems must "be effectively overseen by natural persons during the period in which they are in use," and an overseer must be able to "interrupt the system through a 'stop' button or a similar procedure" (European Union, 2024). Human approval "is a standard way to limit egregious failures," and a user should be able to "activate a graceful shutdown procedure for its agent at any time" (Shavit et al., 2023). Neither duty nor stop button changes what the model read before the person looked. Substitution does, because the approved value is released to a destination and never enters the context the model works in.
What the record shows
Browser Use's pages state plainly what its sensitive data feature does: "The LLM only sees placeholders (x_user, x_pass)" and real values go "directly into form fields after the LLM call" (Browser Use, 2026), a control where the agent types. Those pages do not describe the screen after login, where the bank account, address and balance sit in text the model reads. A render-layer control replaces those values with tokens on the page before the model reads it, which is the job RedactSure, as the Browser Use alternative for agent work, does for this record, with the account released only when the named controller approves the change. Browser Use's real coverage is the input field and the allowlist, and both are worth having. A team can test this on one billing screen: replay the model's input field by field, then trace the account back to the approval that released it.
Methodology and limitations
This page rests on Browser Use's sensitive data documentation, the OpenAI governance paper, Brave's study of an agentic browser, Anthropic's browser-use research, Chase's business access overview, GDPR Article 5(1)(c), EU AI Act Article 14, the AI Agent Index entry for Breeze Agents, and existing RedactSure research pages. Sources were read between October 21, 2026 and October 22, 2026. Vendor statements are quoted in the vendor's own wording, and public documentation read in full gives a sound view of where each product applies its control; not described means the pages reviewed.
The products on this page are described from documentation, and none was tested for this page. No customer of either product is described, and the billing portal is a record type. Both products change, and the page carries its date. RedactSure is deploying now with enterprise and government customers. Compliance determinations belong to the organization's counsel, assessors and security team, and nothing here replaces a pilot of one workflow on the organization's own screens.
Frequently asked questions
Does Browser Use keep a customer's account details out of the model's context?
Only for values the agent types. Its page puts placeholders in the input text and injects real values into the form after the LLM call, so a typed credential stays out of the prompt. The account page after login is not part of that exchange, and the page does not say the model receives tokens instead of the values it displays.
Is a domain allowlist enough to protect a sensitive screen?
No. The allowlist narrows where the agent may go, so a page outside the approved domains never renders, which reduces reach. It does not change what the approved pages show. A bank account on file, a balance and a billing address are displayed by the portal the agent is permitted to visit, and the allowlist has no view of that text.
Who approves the agent's action when it changes an autopay account?
Not from the pages reviewed. The sensitive data page describes substitution and injection, and names no approval step for the submission. An approval default is not a named approver either: the AI Agent Index records an agent whose "default is that approval is required," a setting a user can switch off (AI Agent Index, 2026). The buyer should ask who is named when the change goes through.
What record shows what the model read during a run?
Not from the pages reviewed. The documentation describes the page and the substitution, and no export of the model's input. The AI Control Record keeps every screen as tokens, every action and every approval, and exports to the customer's SIEM, so a reviewer can compare what the model received with what the person approved. Ask for a sample of that record before a reviewer asks for one.
Does turning off screenshots remove the exposure?
No. "Set use_vision=False to prevent screenshot leaks" removes the image path, and the page text still reaches the model as text. A defense that still admits some attacks leaves the value in reach: Anthropic reports that "A 1% attack success rate, while a significant improvement, still represents meaningful risk" (Anthropic, 2025). The screen a model reads is the place to remove the value.
Is typing-time substitution the same as render-time substitution?
No. Typing-time substitution decides what sits in a field the agent fills: the model sees a placeholder, and the library injects the real value after the call. Render-time substitution decides what value appears on the page before the model reads it, whether or not the agent typed anything. A login form needs the first. An account page that displays a bank account needs the second.
How RedactSure closes the typing-time gap in a browser library
Browser Use covers the values an agent types, while the page it reads still displays the account on file and the balance. Real values are "injected directly into form fields after the LLM call," after typing and before the screen is read.
The environment that enforces this is built by RedactSure, an AI agent controls, governance and data protection company.
Before: the agent opens the customer account page under the controller's session, and the model receives the bank account, billing address and balance. Inside: the same page shows ACCT_001 and ADDR_001, with the balance, which the autopay change does not use, as BAL_001, and the account number reaches the autopay form only when the named controller approves the change.
RedactSure is deploying now with enterprise and government customers.
Everyone else controls where data can go. RedactSure controls what the AI can see.
See it on your own workflow
Protecting the sensitive data an agent could read on a screen is a needed part of any solution that includes Browser Use. Bring one billing-portal workflow and see, with test values, the screen the model reads and the approval it waits on. Book a demo