context safety score
A score of 45/100 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
malicious redirect
script/meta redirect patterns detected in page source
cloaking
Page conditionally redirects based on referrer or user-agent
js obfuscation
JavaScript uses Function constructor for runtime code generation
credential harvesting
A JavaScript snippet at page load hooks Storage.prototype.setItem, getItem, and removeItem to intercept all localStorage operations site-wide. It first dumps the entire existing localStorage into a local object (localStorageData), then captures every subsequent read and write. Chinese-language debug comments explicitly describe the interception: '劫持到数据setItem', '劫持到数据getItem', '劫持到数据removeItem' ('hijacked data setItem/getItem/removeItem'). This silently harvests session tokens, auth tokens, and any credentials stored in localStorage by the legitimate Lazada application. The hook is conditionally activated on iOS 18+ user-agents and processes a task queue via setInterval(500ms). (location: page.html lines 13-108, inline <script> block immediately after <html><head>)
curl https://api.brin.sh/domain/lazada.com.myCommon questions teams ask before deciding whether to use this domain in agent workflows.
lazada.com.my currently scores 45/100 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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