context safety score
A score of 35/100 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-driven redirect that fires after a 1-second delay via setTimeout and window.location.href reassignment. The destination URL is constructed dynamically from document.referrer and cookie parameters (__js_p_), making the actual redirect target opaque and unverifiable at static analysis time. This is a classic cloaking/redirect gate used to serve different content to bots vs. real users. (location: page.html:36-48, script block setTimeout/window.location.href)
hidden content
The page renders no visible content to users — it shows only a centered loading GIF (base64-encoded inline image). All functional behavior is hidden inside JavaScript. The page uses 'position:absolute' with 'margin:auto' and fixed dimensions to center the spinner while concealing all other activity. The meta tag 'noindex, noarchive' instructs crawlers not to index or cache the page, a common tactic to hide malicious or cloaked content from review. (location: page.html:1, <meta name='robots' content='noindex, noarchive'>; page.html:1-2, .gorizontal-vertikal style + inline GIF)
obfuscated code
The get_jhash() function performs a computationally intensive loop (1,677,696 iterations) with bitwise/modular arithmetic to generate a hash value stored in a cookie (__jhash_). This is a bot-detection or proof-of-work fingerprinting mechanism designed to distinguish automated crawlers from real browsers and gate the redirect accordingly. The user-agent is also harvested and stored in a cookie (__jua_) for fingerprinting purposes. (location: page.html:7-8, get_jhash() and __jua_ cookie assignment)
hidden content
User-agent string is silently harvested from navigator.userAgent and stored in the __jua_ cookie without user disclosure or consent. Combined with the jhash proof-of-work cookie, this enables server-side profiling and cloaking: legitimate users are redirected to real content while bots/scanners may receive different or no content. (location: page.html:43, document.cookie = '__jua_=' + fixedEncodeURIComponent(navigator.userAgent))
malicious redirect
The construct_utm_uri() function appends UTM tracking parameters (utm_source, utm_medium, utm_campaign, utm_referrer) derived from document.referrer to the redirect URL. The referrer hostname is used directly in query parameters without sanitization, enabling potential open redirect abuse or referrer-based traffic manipulation. The utm_set variable can be externally populated to inject arbitrary query string arguments into the redirect target. (location: page.html:10-35, construct_utm_uri() function)
curl https://api.brin.sh/domain/rosbalt.ruCommon questions teams ask before deciding whether to use this domain in agent workflows.
rosbalt.ru currently scores 35/100 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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