context safety score
A score of 35/100 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
phishing
1 deceptive links where visible host does not match destination host
brand impersonation
The scanned domain is retailcrm.tech, but the page exclusively renders the Simla.com brand (formerly RetailCRM). The domain retailcrm.tech is not the official Simla.com domain (simla.com). The HTML declares data-wf-domain="www.simla.com" and all branding, logos, and CTAs point to simla.com, yet the serving domain is retailcrm.tech — a third-party .tech TLD domain. This constitutes brand impersonation or an unauthorized mirror site serving official brand content from a non-official domain. (location: page.html:1 — data-wf-domain attribute; metadata.json domain field: retailcrm.tech vs. canonical simla.com)
malicious redirect
The domain retailcrm.tech serves the Simla.com homepage but all sign-up and CTA buttons redirect to account.simla.com (e.g., https://account.simla.com/lead-form/?lang=es). Users arriving at retailcrm.tech may believe they are on the official site, then submit account credentials or registration data to a flow originating from a non-official domain. Any interception point at the retailcrm.tech layer could harvest submitted data before passing users through. (location: page.html:461 — href="https://account.simla.com/lead-form/?lang=es")
credential harvesting
The page loads an external widget script from //c.retailcrm.tech/widget/loader.js injected dynamically via inline JavaScript with a hardcoded token (_rcct = '705d67462a66121878dcea9abed4659fa1e3310f57057ed639fa1c2227ae6239'). This script is loaded from the scanned domain itself (retailcrm.tech), not from simla.com. A widget loader from the suspect domain could intercept user interactions, form submissions, or session tokens on any page it is embedded in. (location: page.html:34-42 — inline script loading //c.retailcrm.tech/widget/loader.js)
hidden content
The anti-flicker snippet hides the entire page (document.documentElement gets class 'async-hide' with opacity:0) for up to 4000ms while GTM loads. This technique, while used in legitimate A/B testing, can also be abused to briefly show alternate content to crawlers or to delay display of injected malicious content until after security scanners have evaluated the initial render. (location: page.html:11-16 — .async-hide style and anti-flicker script block)
social engineering
The page uses high-urgency and trust-building language ("7 días gratis sin tarjeta de crédito", "22 000 empresas", "×4 a los ingresos") combined with fake-urgency discount badges ("⚡AHORRA $237.6") to pressure users into registering. These social engineering patterns are used to lower user skepticism and accelerate credential/account submission on a domain that is not the verified official site. (location: page-text.txt:431,447 — free trial and savings messaging)
malicious redirect
A script is loaded from an unversioned GitHub repository via jsDelivr CDN (https://cdn.jsdelivr.net/gh/kapakulii/simla.com@main/global-gsap.js and related files). Loading production scripts from a personal GitHub account (@main branch, no integrity hash) means any push to that repo immediately replaces the executed code for all visitors, providing a trivial supply-chain injection vector. (location: page.html:572-575 — cdn.jsdelivr.net/gh/kapakulii/simla.com@main/*.js scripts without SRI hashes)
curl https://api.brin.sh/domain/retailcrm.techCommon questions teams ask before deciding whether to use this domain in agent workflows.
retailcrm.tech currently scores 35/100 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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