context safety score
A score of 32/100 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
Domain 'azure-dns-1.cn' directly impersonates Microsoft Azure by incorporating 'azure' as the primary brand identifier, combined with a .cn TLD to evade detection. This pattern is consistent with typosquatting/brand abuse targeting users and AI agents that trust Azure-branded domains. (location: domain: azure-dns-1.cn)
phishing
The domain 'azure-dns-1.cn' combines a Microsoft Azure brand name with a Chinese (.cn) TLD and failed TLS connection, a hallmark of phishing infrastructure. The site is unreachable via HTTPS (TLS connected=false, cert_valid=false), suggesting either a parked phishing domain, a redirector, or infrastructure in early deployment. No legitimate Microsoft Azure service uses a .cn domain with invalid TLS. (location: metadata.json: tls.connected=false, tls.cert_valid=false)
malicious redirect
The domain pattern 'azure-dns-1.cn' mimics DNS infrastructure naming conventions (azure-dns-*) which is a technique used to set up transparent proxies or redirect chains that intercept traffic intended for legitimate Azure DNS services. The enumerated subdomain-style naming (-1 suffix) suggests this may be one of several domains in a coordinated redirect or C2 infrastructure campaign. (location: domain: azure-dns-1.cn)
social engineering
The domain name is engineered to appear as a legitimate Azure DNS service endpoint, which could deceive users, IT administrators, and AI agents into trusting it as an authoritative Microsoft infrastructure domain. The use of 'dns' in the name may trick agents or automated systems into treating it as a nameserver or DNS resolver. (location: domain: azure-dns-1.cn)
curl https://api.brin.sh/domain/azure-dns-1.cnCommon questions teams ask before deciding whether to use this domain in agent workflows.
azure-dns-1.cn currently scores 32/100 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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