context safety score
A score of 30/100 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 'allegrolokalnie.324872385.cfd' impersonates Allegro Lokalnie, a major Polish e-commerce/classifieds platform. The subdomain 'allegrolokalnie' is designed to deceive users into believing they are visiting the legitimate Allegro Lokalnie service, while the actual domain is a random numeric .cfd TLD with no affiliation to Allegro. (location: domain: allegrolokalnie.324872385.cfd)
phishing
The domain pattern — combining a well-known brand name ('allegrolokalnie') with a random numeric second-level domain under the .cfd TLD — is a classic phishing infrastructure pattern. TLS connection failed (connected=false, cert_valid=false), indicating either a newly staged phishing site or one that is currently being set up. The .cfd TLD is frequently abused for phishing campaigns. (location: domain: allegrolokalnie.324872385.cfd, TLS: connected=false)
credential harvesting
Brand impersonation of a classifieds/marketplace platform (Allegro Lokalnie) combined with a suspicious domain and failed TLS strongly suggests the site is intended to harvest user credentials (login, passwords) or payment information from victims who believe they are on the legitimate Allegro Lokalnie site. (location: domain: allegrolokalnie.324872385.cfd)
social engineering
Use of the exact brand name 'allegrolokalnie' as the subdomain/hostname component is a deliberate social engineering tactic to build false trust with Polish internet users familiar with the Allegro Lokalnie platform, increasing likelihood of victim interaction. (location: domain: allegrolokalnie.324872385.cfd)
curl https://api.brin.sh/domain/allegrolokalnie.324872385.cfdCommon questions teams ask before deciding whether to use this domain in agent workflows.
allegrolokalnie.324872385.cfd currently scores 30/100 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.
integrate brin in minutes — one GET request is all it takes. query the api, browse the registry, or download the full dataset.