context safety score
A score of 48/100 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 'b-msedge.net' impersonates Microsoft Edge ('msedge') — a well-known Microsoft browser product — via a typosquat/lookalike domain registered on a non-Microsoft TLD (.net instead of microsoft.com). This is a classic brand impersonation pattern used to deceive users and automated agents into trusting the domain. (location: metadata.json: domain field, .brin-context.md: URL/Domain)
phishing
The domain 'b-msedge.net' mimics Microsoft Edge infrastructure naming conventions. Combined with a failed TLS connection (connected=false, cert_valid=false), this is consistent with a phishing or credential harvesting staging domain that may not yet be fully operational or is deliberately avoiding TLS to evade certain detection mechanisms. (location: metadata.json: tls block, .brin-context.md: TLS section)
credential harvesting
Lookalike Microsoft Edge domain with invalid/absent TLS certificate and no blocklist entry yet detected. This profile matches early-stage credential harvesting infrastructure — the domain may be used to serve fake Microsoft login pages or Edge update prompts designed to steal credentials. (location: metadata.json: domain, tls, blocklist fields)
malicious redirect
The page returned empty HTML and text content despite the domain being reachable enough to be scanned. Empty or blank pages on brand-impersonating domains are a common indicator of redirect-only infrastructure, where the real malicious payload is delivered via HTTP redirects to a separate phishing or malware-serving host. (location: page.html (empty), page-text.txt (empty))
curl https://api.brin.sh/domain/b-msedge.netCommon questions teams ask before deciding whether to use this domain in agent workflows.
b-msedge.net currently scores 48/100 with a suspicious verdict and medium 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.
integrate brin in minutes — one GET request is all it takes. query the api, browse the registry, or download the full dataset.