context safety score
A score of 48/100 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
brand impersonation
The scanned domain is fullswingapps.com, but the page content, canonical URL, OG metadata, schema.org markup, and all internal links consistently reference www.fullswinggolf.com — the legitimate Full Swing Golf brand. The serving domain (fullswingapps.com) is not the brand's official domain, yet the page presents itself as the official site with title 'Home - Full Swing Golf Simulators | Champion Proven Technology | Official Site'. This is a classic brand impersonation pattern where an alternate domain serves content that mimics or proxies the legitimate brand site. (location: metadata.json: domain=fullswingapps.com; page.html line 1: canonical href=https://www.fullswinggolf.com/, og:url=https://www.fullswinggolf.com/)
malicious redirect
The page is served from fullswingapps.com but all canonical, OG, and schema URLs point to fullswinggolf.com. This domain mismatch indicates the site may be acting as a proxy or redirect layer in front of the legitimate brand, potentially intercepting user interactions (form submissions, checkout flows) before passing traffic along. (location: page.html line 1: link rel=canonical href=https://www.fullswinggolf.com/; metadata.json: url=https://fullswingapps.com)
credential harvesting
The page integrates Affirm (buy-now-pay-later) checkout JavaScript loaded from cdn1.sandbox.affirm.com — notably the sandbox environment endpoint rather than the production CDN. A sandbox Affirm integration on a site impersonating a brand could be used to intercept payment/financial credential flows under the guise of a legitimate checkout experience. The public_api_key ZY5CSWJ2TJLAL74I is embedded in plaintext. (location: page.html lines 8-12: _affirm_config with script=https://cdn1.sandbox.affirm.com/js/v2/affirm.js)
hidden content
The page contains 70 lines of HTML comments consisting solely of React/Next.js server rendering boundary markers ($!, /$, $) with no visible text. While these are standard Next.js streaming SSR comment markers, their volume (70 entries) is notable and confirms significant portions of page structure are comment-delimited, which could obscure injected content from simple text extractors. (location: page-hidden.txt lines 1-70)
curl https://api.brin.sh/domain/fullswingapps.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
fullswingapps.com currently scores 48/100 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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