context safety score
A score of 41/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
encoded payload
suspicious base64-like blobs detected in page content
cloaking
Page loads content in transparent or zero-size iframe overlay
malicious redirect
The page was fetched from greenbids.ai but all canonical URLs, og:url, schema.org data, and all internal links resolve to perion.com. The HTML title, meta tags, and structured data all declare this as a perion.com page ('Outmax: AI Execution Agent - Perion'), yet the scan origin domain is greenbids.ai. This indicates the content served at greenbids.ai is either proxying or cloaking perion.com content, presenting a foreign domain as a different brand's website. (location: metadata.json domain=greenbids.ai vs page.html canonical href=https://perion.com/outmax-performance-algo/ and og:url=https://perion.com/outmax-performance-algo/)
brand impersonation
The page fully impersonates Perion Network (perion.com) — including their logo, navigation, footer copyright '© Perion Network Ltd.', structured data identifying the organization as Perion, and all branding assets — while being served from the unrelated domain greenbids.ai. This constitutes brand impersonation of a legitimate publicly-traded ad-tech company. (location: page.html lines 19-34, 210, 512; metadata.json domain=greenbids.ai)
social engineering
The page promotes a 'Join our newsletter' form via HubSpot (portalId 8765311) and multiple 'Contact Us' / 'Connect With Us' CTAs. When combined with the domain mismatch (greenbids.ai serving perion.com content), these forms could harvest contact information from users who believe they are interacting with the legitimate Perion Network website. (location: page.html lines 447-454, 269, 441)
credential harvesting
A HubSpot newsletter/contact form is embedded via hbspt.forms.create() with hardcoded portalId and formId. Given the domain spoofing context (greenbids.ai impersonating perion.com), any data submitted through this form would be sent to a potentially adversarial HubSpot account rather than the legitimate Perion organization. (location: page.html lines 448-454; page-text.txt lines 248-252)
hidden content
A 1x1 pixel LinkedIn tracking pixel is injected with display:none styling, invisibly collecting visitor data for LinkedIn Campaign Manager account pid=8180697. While LinkedIn pixels are common, in the context of a domain spoofing scenario this tracking benefits whoever controls the greenbids.ai deployment rather than the impersonated brand. (location: page.html line 586: img height=1 width=1 style=display:none src=https://px.ads.linkedin.com/collect/?pid=8180697)
hidden content
A zero-dimension GTM noscript iframe (height=0, width=0, display:none, visibility:hidden) is present, silently loading Google Tag Manager container GTM-WJ948BJP for users without JavaScript. This enables tracking/data collection that is invisible to the user. (location: page.html lines 203-204; page-text.txt lines 3-4)
curl https://api.brin.sh/domain/greenbids.aiCommon questions teams ask before deciding whether to use this domain in agent workflows.
greenbids.ai currently scores 41/100 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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