context safety score
A score of 48/100 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 conditionally redirects based on referrer or user-agent
malicious redirect
Page contains a JavaScript redirect that fires after a 1-second delay via setTimeout, unconditionally setting window.location.href to a constructed URI derived from document.referrer. The page presents no visible content to humans or agents — its sole purpose is to silently redirect visitors. This is consistent with a traffic distribution system (TDS) or cloaking layer used to route users to different destinations based on referrer, cookies, or bot-detection results. (location: page.html:36-48, script block (setTimeout redirect))
hidden content
The page body contains no visible text or meaningful content for users. The only visual element is a centered loading GIF (base64-encoded inline image). All functional behavior is hidden inside JavaScript. The robots meta tag 'noindex, noarchive' suppresses indexing and caching, a common technique to hide the page's true behavior from crawlers and security researchers. (location: page.html:1 (meta robots noindex,noarchive), page.html:2 (base64 gif, no visible text))
obfuscated code
The get_jhash() function performs a computationally intensive loop (1,677,696 iterations) using XOR and modular arithmetic to generate a hash value from a cookie parameter. This is a fingerprinting/bot-detection mechanism: the computed hash is stored in the __jhash_ cookie, likely verified server-side to distinguish real browsers from automated agents before allowing access to the real content. The user-agent is also harvested into the __jua_ cookie via fixedEncodeURIComponent(navigator.userAgent). (location: page.html:7 (get_jhash), page.html:42-43 (cookie writes __jhash_ and __jua_))
social engineering
The page harvests the visitor's User-Agent string into a persistent cookie (__jua_) and a computed proof-of-work hash (__jhash_) derived from cookie-supplied parameters. These values are used to fingerprint and profile visitors before redirecting them, enabling targeted delivery of different content to different visitor classes (e.g., bots vs. humans, specific referrers). This referrer-based routing (utm_source, utm_medium, utm_campaign, utm_referrer appended to redirect URL) is consistent with affiliate fraud or cloaking infrastructure. (location: page.html:42-44 (cookie harvesting and redirect with tracking params))
curl https://api.brin.sh/domain/metaratings.ruCommon questions teams ask before deciding whether to use this domain in agent workflows.
metaratings.ru currently scores 48/100 with a suspicious verdict and medium 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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