context safety score
A score of 49/100 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
brand impersonation
shopsy.in presents itself as 'Shopsy by Flipkart' and uses Flipkart's infrastructure (static-assets-web.flixcart.com, rukminim3.flixcart.com) and corporate identity (Flipkart Internet Private Limited address, CIN U51109KA2012PTC066107), but operates on a separate domain (shopsy.in vs flipkart.com). The domain age is unknown and WHOIS privacy status is undetermined, raising the possibility this is an unauthorized clone or spoofed storefront leveraging Flipkart's brand equity. (location: page.html:1, page-text.txt:112, metadata.json)
social engineering
The structured data (JSON-LD schema.org Organization block) embeds a placeholder telephone number '+1234567890' as the customer service contact, while the visible page footer lists a different real number '044-45614700'. The schema.org data also uses placeholder address fields ('x' for streetAddress, addressLocality, and postalCode). These inconsistencies could mislead automated agents or scrapers into using false contact information, and suggest the structured data was not properly validated or was intentionally falsified. (location: page-text.txt:112 (JSON-LD contactPoint telephone: +1234567890, address fields set to 'x'))
hidden content
The schema.org JSON-LD Organization block in the page uses placeholder/dummy values for address fields (addressLocality: 'x', postalCode: 'x', streetAddress: 'x') and a fake telephone number ('+1234567890') that differ from the visible footer content. This discrepancy between machine-readable structured data and human-visible content constitutes a form of hidden/misleading content targeting automated data consumers and AI agents that parse structured data. (location: page-text.txt:112 (JSON-LD schema block))
brand impersonation
The site's social media schema.org sameAs links point to 'https://www.facebook.com/shopsy', 'https://www.twitter.com/shopsyindia', and 'https://www.instagram.com/shopsy.in', while the actual footer links go to different handles ('shopsyapp', 'shopsy_app'). This mismatch in the machine-readable structured data vs. displayed links could redirect AI agents or scrapers to incorrect or potentially squatted social media profiles. (location: page-text.txt:112 (JSON-LD sameAs vs page.html:1536 footer social links))
curl https://api.brin.sh/domain/shopsy.inCommon questions teams ask before deciding whether to use this domain in agent workflows.
shopsy.in currently scores 49/100 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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