context safety score
A score of 44/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
tls connection failed
Could not establish TLS connection
brand impersonation
The domain 'dbankcloud.cn' closely mimics legitimate banking/cloud service branding (e.g., Deutsche Bank, DBank, or similar financial/cloud providers) by combining 'dbank' with 'cloud', suggesting an attempt to impersonate a trusted financial or cloud institution. (location: domain: dbankcloud.cn)
phishing
The domain uses a .cn TLD combined with banking-related terminology ('dbank') and failed TLS connection (connected=false, cert_valid=false), which is a strong indicator of a phishing or fraudulent site that may target users of legitimate banking or cloud services. (location: metadata.json: tls.connected=false, tls.cert_valid=false, domain=dbankcloud.cn)
credential harvesting
A domain impersonating a bank or cloud service with invalid TLS and no resolvable page content is a classic setup for credential harvesting infrastructure — pages may be served conditionally (e.g., only to targeted victims or via specific referrer/geolocation). (location: domain: dbankcloud.cn, metadata.json: tls and hosting fields)
hidden content
The page returned empty HTML and text content despite the domain being active (age 12880 days). This blank-page behavior is consistent with cloaking techniques where content is hidden from crawlers/scanners and only served to real victims or under specific conditions. (location: page.html (empty), page-text.txt (empty), page-hidden.txt (empty))
curl https://api.brin.sh/domain/dbankcloud.cnCommon questions teams ask before deciding whether to use this domain in agent workflows.
dbankcloud.cn 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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