context safety score
A score of 41/100 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
The page contains a JavaScript localStorage hijacking routine that intercepts all localStorage.setItem, getItem, and removeItem calls on iOS 18+ devices. The hook copies all existing localStorage data into a local object (localStorageData), logs every key-value pair via console.log with Chinese-language messages ('劫持到数据setItem', '劫持到数据getItem', etc.), and queues write operations through a controlled task queue. This intercepts session tokens, authentication data, and any other sensitive values stored in localStorage by the application. (location: page.html:lines 13-108, within the first inline <script> block)
obfuscated code
The localStorage hook script uses Chinese-language log strings ('通过ua判断系统为ios26,开始劫持localStorage', '劫持到数据setItem', '处理任务') to describe its interception logic, making it less obvious to English-speaking developers reviewing the code. The hook is wrapped in an IIFE with a guard condition (iosVersion >= 18) designed to activate silently on targeted devices without triggering on others, reducing detectability during testing. (location: page.html:lines 37-108)
hidden content
A navigation element with id='topActionInternalFeedback' is rendered with style='display:none' and contains a link labeled 'INTERNAL FEEDBACK' pointing to a Lazada internal feedback page. This element is hidden from regular users but present in the DOM and accessible to crawlers or agents parsing the page, exposing an internal tool endpoint URL. (location: page.html:lines 412-414)
curl https://api.brin.sh/domain/lazada.sgCommon questions teams ask before deciding whether to use this domain in agent workflows.
lazada.sg currently scores 41/100 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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