context safety score
A score of 39/100 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 executes a JavaScript redirect via window.location.href after a 1-second delay, sending the visitor to a dynamically constructed URL derived from the referrer and cookie parameters. The destination is not fixed or disclosed in the page source, enabling opaque redirection to arbitrary targets based on server-set cookie values (__js_p_). (location: page.html:44 — window.location.href = construct_utm_uri(disable_utm))
hidden content
The page renders no visible content to the user — only a centered loading GIF (base64-encoded inline image). All functional logic is hidden inside a script block. The noindex/noarchive robots meta tag suppresses archiving and indexing, concealing the page's behavior from crawlers and security scanners. (location: page.html:1 — <meta name="robots" content="noindex, noarchive">)
obfuscated code
The get_jhash() function performs a computationally intensive hash loop (1,677,696 iterations) to derive a value written to the __jhash_ cookie. This pattern is characteristic of bot/browser fingerprinting or anti-analysis obfuscation used in traffic distribution systems (TDS) and cloaking infrastructure to differentiate automated scanners from real users before deciding redirect destination. (location: page.html:7 — function get_jhash(b))
obfuscated code
The script reads multiple parameters from a cookie named __js_p_ (code, age, sec, disable_utm) that are set server-side and not visible in the HTML. This server-controlled cookie drives the redirect target, TTL, and security flags, allowing the server to deliver different behavior to different visitors (cloaking) without any client-side evidence of the final destination. (location: page.html:37-40 — get_param("__js_p_", ...))
hidden content
The navigator.userAgent is harvested and stored in the __jua_ cookie, then implicitly transmitted to the server on subsequent requests. This enables server-side bot detection and behavioral profiling to selectively expose malicious content only to human visitors while showing benign responses to automated scanners. (location: page.html:43 — document.cookie = "__jua_=" + fixedEncodeURIComponent(navigator.userAgent))
social engineering
The page presents only a loading spinner (animated GIF) with no text, tricking the visitor into waiting through an automatic redirect without any indication of where they are being sent. This pattern is used in traffic distribution systems to silently funnel users to phishing, ad fraud, or malware pages. (location: page.html:2 — base64-encoded GIF rendered as sole visible content)
curl https://api.brin.sh/domain/tricolor.ruCommon questions teams ask before deciding whether to use this domain in agent workflows.
tricolor.ru currently scores 39/100 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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