context safety score
A score of 45/100 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 executes after a 1-second delay via setTimeout. It reads parameters from cookies (__js_p_), computes a hash via get_jhash(), sets tracking cookies (__jhash_ and __jua_), captures the user agent string, and then redirects the browser to a constructed URL via window.location.href. The destination is determined dynamically from cookie values injected server-side, making the final redirect target opaque and unverifiable at scan time. (location: page.html:36-48 (setTimeout block))
hidden content
The page presents no visible content to the user — only a centered 66x66px GIF image (encoded inline as a base64 data URI) and JavaScript. The meta tag 'noindex, noarchive' instructs search engines and archiving services not to index or cache the page, concealing its behavior from crawlers and security researchers. The entire functional payload is hidden within JavaScript logic. (location: page.html:1 (meta robots noindex,noarchive) and page.html:2 (inline base64 GIF))
obfuscated code
The get_jhash() function performs a computationally intensive loop (1,677,696 iterations) over an arithmetic expression with no clear functional purpose beyond producing a derived integer from a cookie-supplied seed. This pattern is characteristic of bot-detection fingerprinting or anti-analysis obfuscation. The function's output is stored in a cookie (__jhash_) and likely used server-side to gate or direct the redirect target. The user agent is also harvested and stored in cookie __jua_ using fixedEncodeURIComponent. (location: page.html:7 (get_jhash function), page.html:43 (__jua_ cookie assignment))
social engineering
The page harvests referrer information (document.referrer) and appends it as utm_source, utm_medium, utm_campaign, and utm_referrer query parameters to the redirect URL. This referrer tracking combined with user-agent harvesting enables profiling of visitors prior to redirection, which is a technique used in traffic distribution systems (TDS) to route victims to different payloads based on their origin or browser environment. (location: page.html:10-31 (construct_utm_uri and referrer tracking logic))
curl https://api.brin.sh/domain/atol.ruCommon questions teams ask before deciding whether to use this domain in agent workflows.
atol.ru currently scores 45/100 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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