context safety score
A score of 43/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
phishing
3 deceptive links where visible host does not match destination host
brand impersonation
The website impersonates the Lazada e-commerce platform by copying its HTML layout, CSS files, and SVG icons to disguise an online gambling operation. (location: Global HTML structure, <link> tags fetching Lazada assets, and embedded SVG symbols)
social engineering
The site uses a deceptive fake product listing to promote a gambling scheme ('PAS77'), making false promises of guaranteed wins ('mudah menang pasti win 77 juta'). (location: <title> tag, <meta name="description">, and visible product details)
malicious redirect
The primary call-to-action buttons ('LOGIN' and 'DAFTAR') redirect users away from the main domain to an external Cloudflare Pages site (bordermovement.pages.dev). (location: Anchor tags wrapping the 'LOGIN' and 'DAFTAR' buttons)
hidden content
A massive block of hidden e-commerce category links (e.g., 'Peralatan Elektronik') is injected into the HTML for SEO poisoning and keyword stuffing. (location: Hidden dropdown menus (.lzd-site-menu-nav-category) and page-hidden.txt)
curl https://api.brin.sh/domain/bordermovement.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
bordermovement.com currently scores 43/100 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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