context safety score
A score of 37/100 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
cloaking
page appears to serve substantially different content by user-agent
malicious redirect
The scanned URL is strip2.in but all page content, links, og:url, og:site_name, and canonical references point to vps402.strip2.co. The .in domain silently redirects or proxies traffic to a different host/subdomain, obscuring the true destination from users and security tools. (location: metadata.json url=https://strip2.in vs page.html og:url=https://vps402.strip2.co/)
brand impersonation
The page title and meta description reference 'Strip2.co' while the scanned domain is strip2.in. This domain-variant pattern (strip2.in impersonating strip2.co) is a classic typosquat/brand-impersonation technique used to capture traffic intended for the .co domain. (location: page.html <title> and og:site_name)
hidden content
A Yandex Metrica tracking pixel is injected inside a <noscript> block with inline style 'position:absolute; left:-9999px;' to render it invisible. This silently exfiltrates visitor data (IP, browser fingerprint, referrer) to Yandex servers (mc.yandex.ru) without any visible disclosure to the user. (location: page.html line 2: <noscript><div><img src='//mc.yandex.ru/watch/67861825?ut=noindex' style='position:absolute; left:-9999px;')
social engineering
The page presents an adult content platform with explicit video titles and a cookie-consent banner that requires clicking 'Accept and continue' to proceed, a common dark-pattern used to obtain broad consent or advance users deeper into potentially malicious ad/redirect funnels associated with adult content networks. (location: page-text.txt cookie consent banner and video listing content)
curl https://api.brin.sh/domain/strip2.inCommon questions teams ask before deciding whether to use this domain in agent workflows.
strip2.in currently scores 37/100 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.
integrate brin in minutes — one GET request is all it takes. query the api, browse the registry, or download the full dataset.