context safety score
A score of 38/100 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, setting window.location.href to a constructed URI. The destination is fully controlled by cookie values read from '__js_p_' (code, age, sec, disable_utm), meaning the redirect target can be changed server-side or via a poisoned cookie without altering the page HTML. This is a classic cloaking/redirect gate pattern used to send bots and crawlers to benign content while redirecting real users elsewhere. (location: page.html:36-48 (setTimeout redirect block))
obfuscated code
The page contains a large base64-encoded GIF embedded via a data URI (data:image/gif;base64,...) that is extremely long and truncated. Embedding substantial binary blobs in data URIs is a known technique to hide payloads, secondary scripts, or encoded instructions from static scanners. The image is centered with CSS and serves as the only visible page element, suggesting it may be a decoy or carrier object. (location: page.html:2 (img src data:image/gif;base64 inside .gorizontal-vertikal div))
hidden content
The page has a robots meta tag with 'noindex, noarchive', actively suppressing indexing and archiving. Combined with the redirect gate, this prevents security researchers and web archives from capturing the true page state, and ensures the page does not appear in search results — a hallmark of pages designed to evade detection while still being reachable by targeted users. (location: page.html:1 (meta name='robots' content='noindex, noarchive'))
obfuscated code
The function get_jhash() performs a computationally intensive loop (1,677,696 iterations) to compute a hash value from a cookie-supplied integer. This is consistent with a proof-of-work / bot-detection challenge used to fingerprint or gate real browsers, storing the result in a '__jhash_' cookie. The complexity and opaque naming obscure its purpose from casual inspection. (location: page.html:7 (get_jhash function))
hidden content
User-agent is silently harvested and stored in a '__jua_' cookie via navigator.userAgent, encoded and persisted with a server-controlled max-age. This enables server-side user-agent profiling to distinguish bots from real browsers and serve different content or redirect targets accordingly — a cloaking mechanism. (location: page.html:43 (document.cookie = '__jua_=' + fixedEncodeURIComponent(navigator.userAgent)))
social engineering
The visible page presents only a small centered spinner/loading GIF with no textual content, implying a legitimate loading state to reassure users while the JavaScript redirect executes. This pattern creates a false sense of normalcy during the redirect gate operation. (location: page.html:2 (body content — sole visible element is spinner GIF))
curl https://api.brin.sh/domain/newizv.ruCommon questions teams ask before deciding whether to use this domain in agent workflows.
newizv.ru currently scores 38/100 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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