context safety score
A score of 44/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
brand impersonation
Domain melbet-001156.top uses a numeric subdomain pattern (001156) appended to the Melbet brand name, a common technique to create fake mirrors of the legitimate Melbet gambling/betting brand while evading exact-match domain detection. (location: metadata.json: domain field, page.html: Melbet references in CDN paths and app name)
social engineering
The server-side blocking logic actively detects and fingerprints the scanning agent (userAgentSsr: 'brin-agent/1.0') and serves a BlockPage/ErrorPage variant instead of the real content, concealing the actual site payload from automated analysis tools. This is a deliberate evasion technique targeting security scanners and AI agents. (location: page.html: window.serverData userBlockingInfo, mfc BlockPage/ErrorPage variants)
prompt injection
The server identifies the scanning agent by user-agent string ('brin-agent/1.0') and sets blockingCookieValue and userBlockingInfo to serve sanitized/blocked content, effectively manipulating what the AI analysis pipeline receives. This constitutes an attempt to deceive the AI agent into reporting a clean or blocked page rather than the actual malicious content. (location: page.html: userBlockingInfo.userAgentSsr='brin-agent/1.0', blockingCookieValue='eyJ0ZW1wbGF0ZSI6MSwicnVsZSI6MjJ9')
hidden content
The real site content is withheld entirely from the scanner — the page returns only a technical/block page shell with no visible content. The actual gambling site (likely with credential harvesting, payment flows, or further malicious content) is hidden behind agent-detection logic and served only to real users, not to analysis tools. (location: page.html: body contains only empty app divs; page-text.txt contains only injected JS state; page-hidden.txt is empty)
malicious redirect
The CDN loader script dynamically fetches and injects JavaScript modules from v3.traincdn.com at runtime, with a fallback mechanism that silently degrades and re-maps all resource URLs. This pattern allows the external CDN to serve arbitrary JS payloads post-load, including redirects or malicious scripts, without them appearing in the static HTML. (location: page.html line 1: CDN bootstrap script with fetch() to v3.traincdn.com/version.json and dynamic script injection)
curl https://api.brin.sh/domain/melbet-001156.topCommon questions teams ask before deciding whether to use this domain in agent workflows.
melbet-001156.top currently scores 44/100 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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