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
The domain 'arc-dc-msedge.net' is constructed to impersonate Microsoft Edge ('msedge') and potentially Microsoft Azure or Arc services ('arc-dc'). The combination of 'arc', 'dc', and 'msedge' mimics legitimate Microsoft infrastructure naming conventions to deceive users and automated systems into trusting the domain. (location: domain: arc-dc-msedge.net)
phishing
The domain 'arc-dc-msedge.net' uses a .net TLD while impersonating Microsoft Edge/Azure Arc services (legitimate domains use .com/.microsoft.com). This typosquatting/brand-abuse pattern is a strong indicator of a phishing infrastructure site designed to harvest credentials or deliver malware under the guise of a Microsoft service. (location: domain: arc-dc-msedge.net)
malicious redirect
TLS connection failed (connected=false, cert_valid=false) and the page returned empty content. This is consistent with a domain parked or configured as a redirect/relay node in a malicious infrastructure chain, or a C2/phishing domain awaiting activation. The absence of a valid TLS certificate on a domain impersonating a major tech brand is a strong risk indicator. (location: metadata.json: tls.connected=false, tls.cert_valid=false)
social engineering
The domain name 'arc-dc-msedge.net' is crafted to appear as legitimate Microsoft infrastructure (Azure Arc, Domain Controller, Microsoft Edge) to social-engineer both human users and AI agents into treating it as a trusted endpoint. This naming pattern is commonly used in spear-phishing and business email compromise campaigns targeting enterprise environments. (location: domain: arc-dc-msedge.net)
curl https://api.brin.sh/domain/arc-dc-msedge.netCommon questions teams ask before deciding whether to use this domain in agent workflows.
arc-dc-msedge.net 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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