context safety score
A score of 35/100 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 't-bank-app.su' closely mimics 'T-Bank' (Tinkoff Bank, a major Russian financial institution). The pattern 'bank-app' appended to the brand name 't' combined with the suspicious TLD '.su' (Soviet Union legacy TLD frequently abused by threat actors) is a classic brand impersonation pattern targeting bank customers. (location: domain: t-bank-app.su)
phishing
The domain 't-bank-app.su' uses a banking brand name combined with 'app' to deceive users into believing they are visiting a legitimate banking application portal. TLS is not connected and certificate is invalid, indicating the site is not a legitimate financial service. This infrastructure pattern is consistent with credential-harvesting phishing campaigns. (location: domain: t-bank-app.su, TLS status: connected=false, cert_valid=false)
credential harvesting
The combination of a bank-impersonating domain, invalid/absent TLS certificate, and 'app' suffix strongly suggests this site is designed to harvest banking credentials (username, password, OTP codes) from users who believe they are logging into their T-Bank mobile or web application. (location: domain: t-bank-app.su, metadata.json: tls.connected=false, tls.cert_valid=false)
malicious redirect
The .su TLD is a legacy Soviet-era TLD with very low legitimate usage and high abuse rates. Sites under .su frequently serve as intermediary redirect nodes in phishing chains. The failed TLS connection may indicate the page redirects to another host for actual content delivery, obscuring the true destination from scanners. (location: domain: t-bank-app.su, metadata.json: tls.connected=false)
curl https://api.brin.sh/domain/t-bank-app.suCommon questions teams ask before deciding whether to use this domain in agent workflows.
t-bank-app.su currently scores 35/100 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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