context safety score
A score of 39/100 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 'myq-see.com' is a typosquat of the legitimate 'myq.com' (Chamberlain/LiftMaster MyQ smart garage platform). The hyphenated 'myq-see' pattern is a common brand impersonation tactic designed to deceive users into believing the site is affiliated with the official MyQ service. (location: domain: myq-see.com)
phishing
The domain closely mimics the MyQ brand (a widely used IoT/smart home platform) and fails TLS validation (connected=false, cert_valid=false), which is consistent with a phishing site that has not properly configured HTTPS. Legitimate MyQ services enforce valid TLS. Users expecting the real MyQ service could be deceived into submitting credentials. (location: metadata.json: tls block)
credential harvesting
Combination of brand impersonation of a login-dependent IoT service (MyQ requires account credentials to control garage doors/home access), failed TLS, and a relatively young domain (228 days) strongly indicates a credential harvesting setup targeting MyQ account holders. (location: domain: myq-see.com, metadata.json)
malicious redirect
The domain is young (228 days), has unknown hosting reputation, and failed to serve any page content — all files (page.html, page-text.txt, page-hidden.txt) are empty. This may indicate the site is dormant, under construction for future use, or actively cloaking content and only serving payloads to select traffic (e.g., redirecting based on user-agent or referrer), a common technique in phishing and malware campaigns. (location: metadata.json: hosting block, page.html, page-text.txt, page-hidden.txt)
curl https://api.brin.sh/domain/myq-see.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
myq-see.com currently scores 39/100 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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