context safety score
A score of 40/100 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
hidden content
Page renders no visible content to users — only a centered 66x66 GIF image embedded as a base64 data URI. All page logic is purely JavaScript-driven with no human-readable text, navigation, or page body. This cloaking pattern hides the page's true function from casual inspection and some crawlers. (location: page.html:1-2, <style>.gorizontal-vertikal and <body> content)
obfuscated code
The get_jhash() function performs a computationally intensive hash loop (1,677,696 iterations) over a cookie-derived value, then stores the result in a cookie named __jhash_. This is a bot/browser fingerprinting challenge mechanism designed to distinguish automated agents (crawlers, AI agents, security scanners) from real browsers. The algorithm is deliberately opaque and serves as an anti-analysis primitive. (location: page.html:7, get_jhash() function)
hidden content
The page harvests the visitor's User-Agent string via navigator.userAgent and stores it in a cookie named __jua_ using fixedEncodeURIComponent encoding. This fingerprinting data is silently collected without user consent or disclosure and can be used to profile and track agents or users visiting the page. (location: page.html:43, document.cookie = '__jua_=')
malicious redirect
After a 1-second delay, the page unconditionally redirects the visitor via window.location.href = construct_utm_uri(disable_utm). The destination URL is dynamically constructed from the document.referrer, cookie values, and UTM parameters — meaning the redirect target is not statically determinable and could route different visitors (or agents) to different destinations based on fingerprint data stored in __js_p_ cookies. (location: page.html:44, setTimeout -> window.location.href = construct_utm_uri(disable_utm))
obfuscated code
Parameters controlling redirect behavior (code, age, sec, disable_utm) are read exclusively from a cookie named __js_p_ using numeric index positions (0,1,2,4). This indirection hides configuration from static analysis — a server or prior script can inject arbitrary redirect targets and behaviors through this cookie without it being visible in the page source. (location: page.html:37-40, get_param('__js_p_', ...) calls)
hidden content
The robots meta tag is set to 'noindex, noarchive', preventing search engines from indexing or caching the page. Combined with the JavaScript-only redirect, this ensures the true page content and redirect behavior are not preserved in any public archive, making forensic analysis and threat attribution significantly harder. (location: page.html:1, <meta name='robots' content='noindex, noarchive'>)
social engineering
The page presents a visually blank page with only a small centered loading GIF (base64-encoded, 66x66px, created with ajaxload.info per the GIF comment header). This mimics a loading screen to keep users/agents passively waiting during the 1-second delay before the redirect executes, reducing the chance of user intervention or suspicion. (location: page.html:2, base64 GIF with NETSCAPE2.0 animation and 'Created with ajaxload.info' comment)
curl https://api.brin.sh/domain/nic.ruCommon questions teams ask before deciding whether to use this domain in agent workflows.
nic.ru currently scores 40/100 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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