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
The domain feedzai.cloud uses the brand name 'Feedzai' (a well-known fraud detection and financial crime prevention company) under a non-official TLD (.cloud instead of the legitimate feedzai.com). This is a common technique for brand impersonation targeting customers, partners, or employees of Feedzai. (location: domain: feedzai.cloud)
phishing
TLS connection failed (connected=false, cert_valid=false) on a domain impersonating a financial security brand. The combination of brand impersonation via lookalike domain and broken/absent TLS is consistent with a phishing infrastructure setup, possibly still under construction or serving content selectively. (location: metadata.json: tls.connected=false, tls.cert_valid=false)
malicious redirect
The page returned empty HTML and text content despite the domain being live and registered for over 10 years (3912 days). This may indicate the domain serves content conditionally (e.g., based on user-agent, IP geolocation, or referrer), redirecting targeted victims while appearing blank to automated scanners. (location: page.html (empty), page-text.txt (empty))
social engineering
Feedzai is a fraud/AI risk detection company. A lookalike domain (feedzai.cloud) could be used in targeted spear-phishing or business email compromise (BEC) attacks against Feedzai's financial institution clients, exploiting trust in the brand to solicit credentials or sensitive information. (location: domain: feedzai.cloud)
curl https://api.brin.sh/domain/feedzai.cloudCommon questions teams ask before deciding whether to use this domain in agent workflows.
feedzai.cloud 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.