context safety score
A score of 32/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
encoded payload
suspicious base64-like blobs detected in page content
malicious redirect
script/meta redirect patterns detected in page source
cloaking
Page checks user-agent for bot/crawler strings to serve different content
cloaking
Page conditionally redirects based on referrer or user-agent
cloaking
Page loads content in transparent or zero-size iframe overlay
js obfuscation
JavaScript uses Function constructor for runtime code generation
credential harvesting
JavaScript intercepts form input fields (name, phone, email) before form submission and sends them via window.tracker.track() to an external tracking endpoint. Data is captured on every keystroke reset (input event) and on button click, transmitting PII including name, phone number with country code, and email to ClientID 3008 with explicit consent flags set to true without clear user disclosure at point of capture. (location: page.html lines 48-91, inline script block using MutationObserver watching .tn-form__submit .t-submit)
credential harvesting
Multiple third-party analytics scripts loaded from external domains collect visitor data: eu.umami.is, stat.nesmachny.com, app.eurometrics.eu, and app.onecdp.ru. The onecdp.ru script (data-onecdp-id) is a Customer Data Platform loader that silently profiles visitors. These scripts run before any user interaction or consent. (location: page.html lines 48, 153 — script tags loading eu.umami.is/script.js, stat.nesmachny.com/script.js, app.eurometrics.eu/script.js, app.onecdp.ru/script/script.js)
social engineering
The site's stated core business purpose is covert collection of customer data (phone numbers, emails, MAC addresses, gender, age, social network profiles) from people using public guest Wi-Fi without their explicit awareness. Marketing language describes this as 'delicately collecting information about a client who uses public wi-fi' and collecting '85% of data about your customers'. This is the advertised product, not an attack on visitors to this page, but it describes a social engineering infrastructure targeting end-users of customer Wi-Fi networks. (location: page.html lines 163-175, page-text.txt lines 9-12)
credential harvesting
The og:url meta tag references wifly.ru while the page is served from wifly.net. The canonical link also points to wifly.ru. This domain split (wifly.net serving content branded as wifly.ru) could facilitate confusion between the two domains, though both appear to be owned by the same company. (location: page.html line 1 — meta property og:url content='https://wifly.ru' and link rel=canonical href='https://wifly.ru')
curl https://api.brin.sh/domain/wifly.netCommon questions teams ask before deciding whether to use this domain in agent workflows.
wifly.net currently scores 32/100 with a suspicious verdict and low confidence. The goal is to protect agents from high-risk context before they act on it. Treat this as a decision signal: higher scores suggest lower observed risk, while lower scores mean you should add review or block this domain.
Use the score as a policy threshold: 80–100 is safe, 50–79 is caution, 20–49 is suspicious, and 0–19 is dangerous. Teams often auto-allow safe, require human review for caution/suspicious, and block dangerous.
brin evaluates four dimensions: identity (source trust), behavior (runtime patterns), content (malicious instructions), and graph (relationship risk). Analysis runs in tiers: static signals, deterministic pattern checks, then AI semantic analysis when needed.
Identity checks source trust, behavior checks unusual runtime patterns, content checks for malicious instructions, and graph checks risky relationships to other entities. Looking at sub-scores helps you understand why an entity passed or failed.
brin performs risk assessments on external context before it reaches an AI agent. It scores that context for threats like prompt injection, hijacking, credential harvesting, and supply chain attacks, so teams can decide whether to block, review, or proceed safely.
No. A safe verdict means no significant risk signals were detected in this scan. It is not a formal guarantee; assessments are automated and point-in-time, so combine scores with your own controls and periodic re-checks.
Re-check before high-impact actions such as installs, upgrades, connecting MCP servers, executing remote code, or granting secrets. Use the API in CI or runtime gates so decisions are based on the latest scan.
Learn more in threat detection docs, how scoring works, and the API overview.
Assessments are automated and may contain errors. Findings are risk indicators, not confirmed threats. This is a point-in-time assessment; security posture can change.
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