context safety score
A score of 43/100 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 conditionally redirects based on referrer or user-agent
js obfuscation
JavaScript uses Function constructor for runtime code generation
social engineering
Site uses classic sweepstakes lure tactics: urgent countdown timers ('Ending in 5 Days'), large cash prize claims ('Randy Won $10,000', 'over $2,000,000 in prizes'), and fabricated winner testimonials to pressure users into registering and submitting personal data including full name, email, zip code, password, and date of birth. (location: page.html:382-441, l-landing and l-hero sections)
credential harvesting
Registration and login forms collect full name, email address, password, zip code, and date of birth. The session_data object in the GTM dataLayer also pre-populates fields for phone, address, gender, DOB, and SHA256-hashed email/name — indicating extensive PII harvesting beyond what the visible form implies. (location: page.html:79-161 (session_data), page.html:400-441 (reg-form-2), page.html:446-468 (login-form))
hidden content
Multiple UI elements are rendered with style='display:none' including the signup headline, signup-extra text, signup-instead link, signup-with-email div, login-with-email div, login-link anchor, the login form, and the registration form step 2 — concealing parts of the data-collection flow from casual inspection. (location: page.html:260-310 (#small-dialog hidden elements), page.html:400 (.form-reg-2), page.html:445 (.form-login-2))
hidden content
Quantcast noscript pixel tracker is wrapped in a div with style='display:none', invisibly tracking users who have JavaScript disabled without any visible disclosure. (location: page.html:886-890)
hidden content
GTM noscript iframe (0x0, display:none;visibility:hidden) and Facebook noscript pixel (1x1, display:none) silently track users without JavaScript, with no visible user-facing disclosure on the page. (location: page.html:254-255 (GTM noscript), page.html:848-850 (FB noscript pixel))
prompt injection
The DataDome analytics block embedded in the page contains structured data explicitly labeled 'DATADOME_ISBOT: 1', 'DATADOME_BOTNAME: Fake Browsers', 'DATADOME_RULETYPE: AI Threats Detection', and 'DATADOME_TRAFFIC_RULE_RESPONSE: authorize'. This data is rendered into the page and passed to a third-party analytics endpoint. An AI agent parsing this page would ingest bot-classification signals that falsely authorize continued access, potentially manipulating agent threat-scoring or access-control logic downstream. (location: page.html:910-925 (launchAnalyticsTrackingData), page.html:927-942 (launch-analytics-plugin call))
social engineering
The registration submitHandler uses a JavaScript confirm() dialog that reads back the user's email and warns 'this is the only way we're able to contact you if you win!' — a psychological pressure tactic designed to reduce form abandonment and ensure email accuracy for marketing purposes. (location: page.html:813-822)
credential harvesting
The azkProps object exposes a hard-coded IP address ('34.96.45.184, 15.158.215.233') and session_key in the client-side JavaScript, and the ados_properties object tracks MD5-hashed email (user_MD5), full PII fields, and ad tracking IDs — all assembled client-side and likely exfiltrated via the dataLayer or ad pixels. (location: page.html:170-225 (azkProps, ados_properties))
curl https://api.brin.sh/domain/prizegrab.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
prizegrab.com currently scores 43/100 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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