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 'zscalerone.net' directly impersonates Zscaler, a well-known cybersecurity company. The legitimate domain is 'zscaler.com'. The addition of 'one' to the brand name is a classic typosquatting/impersonation technique designed to deceive users and automated agents into trusting the domain as an official Zscaler property. (location: domain: zscalerone.net)
phishing
The domain closely mimics Zscaler's brand ('zscalerone.net' vs 'zscaler.com') and fails TLS validation (connected=false, cert_valid=false), which is consistent with a phishing infrastructure site. The combination of brand impersonation and no valid TLS certificate strongly indicates a phishing operation targeting Zscaler customers or employees. (location: domain: zscalerone.net, metadata.json TLS fields)
credential harvesting
Domains impersonating enterprise security vendors like Zscaler are frequently used for credential harvesting, targeting corporate users who may enter their Zscaler login credentials believing they are on the legitimate portal. The absence of valid TLS and the brand-mimicking domain name are hallmarks of credential harvesting infrastructure. (location: domain: zscalerone.net)
malicious redirect
TLS connection failed entirely (connected=false), suggesting the domain may be parked, inactive, or serving as a redirect node. Impersonation domains are commonly used as intermediate redirect hops in multi-stage phishing chains to obscure the final malicious destination from security scanners. (location: metadata.json: tls.connected=false, tls.cert_valid=false)
curl https://api.brin.sh/domain/zscalerone.netCommon questions teams ask before deciding whether to use this domain in agent workflows.
zscalerone.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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