context safety score
A score of 40/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
tls connection failed
Could not establish TLS connection
phishing
Domain 'kumarpay.in' uses a payment-themed name on an India-specific TLD with no TLS connectivity (TLS connected=false, cert_valid=false). The combination of a financial/payment brand name, failed TLS, and completely empty page content is a strong indicator of a phishing or credential-harvesting site that may be under construction, cloaking content from crawlers, or serving malicious payloads only to targeted victims. (location: https://kumarpay.in / metadata.json)
brand impersonation
The domain 'kumarpay.in' incorporates 'pay' suggesting impersonation of a payment processor or financial brand (e.g. PayTM, Google Pay, PhonePe, or a regional payment service named Kumar Pay). Use of a payment-related brand name on a low-reputation .in domain with no verifiable TLS certificate raises strong brand impersonation risk. (location: https://kumarpay.in / metadata.json domain field)
credential harvesting
A payment-themed domain with failed TLS (no valid certificate, no TLS connection established) serving empty page content is a classic pre-deployment or cloaked credential harvesting setup. Legitimate payment sites always enforce HTTPS with valid certificates; the absence of any TLS here indicates users' credentials or financial data would be transmitted insecurely if the site activates. (location: metadata.json tls block: connected=false, cert_valid=false, san_match=false)
curl https://api.brin.sh/domain/kumarpay.inCommon questions teams ask before deciding whether to use this domain in agent workflows.
kumarpay.in currently scores 40/100 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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