context safety score
A score of 38/100 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 'siege-amazon.com' directly incorporates the Amazon brand name as a subdomain-style suffix. This is a classic brand impersonation pattern designed to deceive users and automated agents into trusting the domain as Amazon-affiliated. (location: metadata.json: domain=siege-amazon.com)
phishing
The domain 'siege-amazon.com' combines a generic prefix with 'amazon.com' lookalike structure. The word 'siege' combined with a major brand name is consistent with adversarial/attack-themed phishing infrastructure targeting Amazon customers or systems. (location: metadata.json: url=https://siege-amazon.com)
malicious redirect
TLS is not connected and cert is invalid (connected=false, cert_valid=false, san_match=false) yet the URL scheme is HTTPS. This configuration is consistent with a site that may redirect to a functional phishing page or use HTTP downgrade attacks, and the page content being empty suggests the real payload may be delivered conditionally or via redirect. (location: metadata.json: tls.connected=false, tls.cert_valid=false, tls.san_match=false)
social engineering
The domain name 'siege-amazon.com' is semantically aggressive — 'siege' implies an attack or takeover context — which may be used in targeted spear-phishing campaigns against Amazon employees, partners, or customers by implying urgency or authority. (location: metadata.json: domain=siege-amazon.com)
curl https://api.brin.sh/domain/siege-amazon.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
siege-amazon.com currently scores 38/100 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.