context safety score
A score of 23/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
brand impersonation
The domain 'doctolib-consultation.com' closely mimics the well-known French medical appointment platform 'Doctolib' (doctolib.fr). The addition of '-consultation' is a classic typosquatting/subdomain-impersonation pattern designed to deceive patients and healthcare users into believing they are interacting with the legitimate Doctolib service. (location: domain: doctolib-consultation.com)
phishing
The domain is 14 days old, uses a DV (domain-validated) Let's Encrypt certificate — the lowest-trust certificate type trivially obtained by threat actors — and impersonates a healthcare brand. This combination is highly characteristic of a phishing infrastructure site staged to harvest patient credentials or personal health information. The current 503 response is consistent with a site in preparation or intermittently active to evade scanners. (location: domain: doctolib-consultation.com, TLS issuer: Let's Encrypt R13, domain_age_days: 14)
credential harvesting
Impersonation of a medical appointment portal (Doctolib) creates a high-risk credential harvesting scenario: users would be likely to enter login credentials, national health ID numbers (Carte Vitale), and personal medical data on a convincing fake login page. The infrastructure is consistent with a credential-harvesting site pending deployment. (location: domain: doctolib-consultation.com)
social engineering
The domain name combines a trusted healthcare brand ('doctolib') with a legitimizing term ('consultation'), exploiting patients' trust in medical services. This social engineering technique lowers victim suspicion and increases the likelihood of credential submission or personal data disclosure. (location: domain: doctolib-consultation.com)
curl https://api.brin.sh/domain/doctolib-consultation.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
doctolib-consultation.com currently scores 23/100 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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