context safety score
A score of 37/100 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 IIFE at page load overrides Storage.prototype.setItem, getItem, and removeItem to intercept and log all localStorage operations for iOS 18+ devices. It pre-dumps all existing localStorage keys/values on initialization and queues every subsequent write. Chinese-language console logs explicitly label the behavior as 'hijacking' (劫持到数据). Session tokens, auth cookies stored in localStorage, and any credentials cached by the browser or AI agents would be exposed. (location: page.html lines 11-109, inline <script> block immediately after <head> open)
obfuscated code
The localStorage hijack code is embedded as an immediately-invoked function expression (IIFE) with Chinese-language internal comments and log strings that obscure its true purpose from English-language reviewers and automated scanners. The code presents as a compatibility shim but performs covert interception. Log strings include '通过ua判断系统为ios26，开始劫持localStorage' (detect iOS 26 and begin hijacking localStorage) and repeated '劫持到数据' (hijacked data) labels. (location: page.html lines 11-109, inline <script> block)
curl https://api.brin.sh/domain/lazada.co.thCommon questions teams ask before deciding whether to use this domain in agent workflows.
lazada.co.th currently scores 37/100 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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