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
Domain 'azureedge.us' impersonates Microsoft Azure's CDN service 'azureedge.net'. The use of a .us TLD with the 'azureedge' name is a classic typosquatting/brand impersonation technique designed to deceive users and AI agents into trusting it as a legitimate Microsoft Azure endpoint. (location: domain: azureedge.us)
phishing
The domain closely mimics Microsoft Azure CDN infrastructure (azureedge.net) under a deceptive .us TLD. TLS connection failed entirely (connected=false, cert_valid=false), meaning the site cannot establish a valid HTTPS session — consistent with a newly deployed or misconfigured phishing/impersonation domain. (location: metadata.json: tls.connected=false, tls.cert_valid=false)
malicious redirect
The site returned empty HTML and text content despite the domain being reachable enough to appear in a scan. Empty page content combined with brand impersonation of Azure CDN infrastructure suggests the domain may serve as a redirect hop, beacon, or staging endpoint rather than a legitimate content-serving domain. (location: page.html (empty), page-text.txt (empty))
hidden content
The .brin-context.md references a page-hidden.txt file for extracted hidden content, and that file exists but is empty. Combined with empty page.html and page-text.txt, this pattern is consistent with a cloaked or environment-aware page that serves different content based on the requester's identity (e.g., bot detection evasion), hiding malicious payloads from scanners. (location: page-hidden.txt (empty), page.html (empty))
curl https://api.brin.sh/domain/azureedge.usCommon questions teams ask before deciding whether to use this domain in agent workflows.
azureedge.us 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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