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
malicious redirect
TLS connection failed (connected=false, cert_valid=false, san_match=false) on domain outfra.xyz. The site cannot establish a valid HTTPS session, which is a strong indicator of a misconfigured or deceptive site potentially used for malicious redirects or credential interception over an insecure channel. (location: metadata.json: tls fields)
phishing
Domain outfra.xyz uses a .xyz TLD — a registrar commonly associated with low-cost throwaway domains used in phishing campaigns. Combined with unknown domain age, redacted WHOIS, and failed TLS, this matches the infrastructure profile of a phishing or social engineering site. (location: metadata.json: domain, tls, whois fields)
social engineering
Domain outfra.xyz has unknown hosting reputation, unknown domain age, and no blocklist entry yet. This zero-history profile is consistent with a newly registered domain set up for a short-lived social engineering campaign before detection. (location: metadata.json: hosting.reputation, whois.domain_age_days, blocklist.listed)
curl https://api.brin.sh/domain/outfra.xyzCommon questions teams ask before deciding whether to use this domain in agent workflows.
outfra.xyz 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.
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