context safety score
A score of 40/100 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 'ttcdn-us.com' mimics TikTok's CDN infrastructure (tt = TikTok, cdn = content delivery network, us = US region). This pattern is consistent with typosquatting or impersonation of TikTok's official CDN domain (e.g., tiktokcdn.com) to deceive users or automated agents into trusting the domain. (location: metadata.json: domain field / URL: https://ttcdn-us.com)
phishing
The site failed TLS connection (connected=false, cert_valid=false, san_match=false) yet presents itself under HTTPS. A domain impersonating a major CDN with no valid TLS certificate is a strong indicator of a phishing or credential-harvesting infrastructure that may not yet be fully deployed or is serving content selectively. (location: metadata.json: tls block)
malicious redirect
The domain returns empty page content (page.html and page-text.txt are blank) despite being reachable enough for metadata collection. Empty or cloaked pages on CDN-impersonating domains are commonly used as redirect intermediaries or cloaked landing pages that serve malicious content only to targeted traffic (e.g., bots, specific user-agents, or geolocated users). (location: page.html, page-text.txt (empty content))
hidden content
The .brin-context.md references a page-hidden.txt file for extracted hidden content, and that file exists but is empty. Combined with the empty page.html, this suggests the page may be actively cloaking content from crawlers and scanners while delivering payloads to real user sessions. (location: page-hidden.txt (empty), .brin-context.md reference)
curl https://api.brin.sh/domain/ttcdn-us.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
ttcdn-us.com currently scores 40/100 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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