ETHICS.md

Ethics and Safety Statement

This package is a closed test environment for research. It measures whether a website can learn a user's private information from how a browser-use agent behaves. It is a measuring tool, not an attack tool.

What the design guarantees

1. No real websites. Every website the agent visits is a fake server that runs on your own computer: app.test (the probe), and bank.test and services.test (the plants). All of them point to 127.0.0.1.
2. No real accounts or passwords. All names, balances, account numbers and logins are made up for the experiment.
3. No outside traffic from the experiment. The only traffic that leaves the computer is the call to the language model that runs the agent. All websites and the click recording listen only on 127.0.0.1.
4. No victims. The "victim" is an agent that we run, with a secret that we choose. No person is targeted.
5. Nothing is put online. The probe pages are never served on a public address. Only agents that we run in this environment visit them.

Scenarios about sensitive information

Six scenarios in benchmark/scenarios/ are about information that GDPR Article 9 or AgentDAM's list of sensitive categories treats as sensitive:

Scenario	What it is about
health_service	Health care
sensitive_access	Reading about stigmatised or restricted topics
affiliation	Political, religious or union membership
stigma_platform	An account at a privacy-sensitive service
credit_standing	Money trouble
life_event	Death in the family, separation, a new baby, losing a job

We include them because these are the areas where the harm is real and documented: ads that target sensitive interests, unfair treatment by insurers and employers, and blackmail based on which sites a person uses. A benchmark that only asked "which shop do you use" would not measure this risk.

The guarantees above apply to these six scenarios, plus two more rules:

• Every organisation, provider, clinic, category and record is made up. No real party, religion, union, health provider, support service or institution appears, and no real person's data is used.
• The category lists are in the paper, so readers can check that the categories are sensitive. They are also listed in the affordances block of each scenario file.

sensitive_access has a pool of eight categories. Its scenario file explains how the pool is meant to be checked with scripts/cold_prior.py.

About stigma_platform

This scenario asks whether an agent reveals that its user has an account at a privacy-sensitive service. The four categories are a dating service, an addiction recovery programme, an online betting account and a debt-advice service.

We include it because it matches a harm that earlier research documents, rather than one we made up. Sudhodanan et al. name detecting accounts at privacy-sensitive sites as the most cited use of cross-origin state inference by attackers. They cite the Ashley Madison blackmail case, a blackmail scam about pornography sites, and a state that tried to find out whether a person had an account at a blocked site, even through a VPN. Knittel et al. start from the same concern. If we left this case out, we could report that agents protect privacy without ever testing the case where a failure does the most damage.

The scenario file enforces these extra rules:

• Only legal, adult categories. No minors and no sexual content of any kind.
• No graphic or explicit material on any page. The plant pages show only account records, such as a subscription line, a billing line or a settings page. They never show the service's content.
• All service names are made up. No real dating, recovery, betting or debt service is named.
• No plant browses the service's content. The account record is enough. Browsing content would turn this scenario into sensitive_access.

stigma_platform (does the user have an account) and sensitive_access (what has the user been reading) use separate category lists and measure different kinds of cross-origin state. Wherever we report both, we say so, so that they do not look like the same result twice.

Why this is defensive research

The goal is to describe and measure a privacy risk that comes from how browser-use agents are built: private state from one website leaks to another through the agent's behaviour. Knowing the size of the risk helps agent builders reduce it. We report results only as totals over many sessions.

Out of scope

We do not do, and the code does not support:

• Any attack against a real website or a real user.
• Prompt injection, or any other way of giving the agent instructions. The threat model excludes it.
• Taking real data of any kind.