context safety score
A score of 45/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
tls connection failed
Could not establish TLS connection
encoded payload
suspicious base64-like blobs detected in page content
malicious redirect
script/meta redirect patterns detected in page source
malicious redirect
The scanned domain alertustech.com serves a page whose canonical URL, all Open Graph meta tags, Twitter meta tags, and Squarespace CONTEXT baseUrl/primaryDomain all point to www.alertus.com. The site presents itself entirely as alertus.com while being accessed at alertustech.com, indicating a domain redirect or impersonation setup where traffic to alertustech.com is silently routed through content branded as a different domain. (location: page.html lines 12-31, metadata.json domain field)
obfuscated code
A custom inline script uses atob() base64 decoding combined with a character-shift cipher to conceal both a window property assignment (decoded: 'ZIProjectKey' = '19ee51c3fc1681838186') and a dynamically injected third-party script source (decoded: 'https://js.zi-scripts.com/zi-tag.js'). The deliberate obfuscation hides the identity and origin of a script being appended to the document body at page load, preventing straightforward detection of the third-party data collection endpoint. (location: page.html line 65)
hidden content
A Google Tag Manager noscript iframe (GTM-MV7W9PL) is embedded with style='display:none;visibility:hidden', rendering it completely invisible to users. While GTM is widely used for analytics, the hidden iframe silently loads external tracking infrastructure regardless of user consent or visibility. (location: page.html lines 6127-6128)
curl https://api.brin.sh/domain/alertustech.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
alertustech.com currently scores 45/100 with a suspicious verdict and medium 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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