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
obfuscated code
The page contains a JavaScript localStorage hijacking hook (lines 13-108 of page.html) that intercepts and overrides Storage.prototype.setItem, getItem, and removeItem. The code activates for iOS 18+ user agents, dumps all existing localStorage contents into a local variable, queues all write/delete operations, and logs every key-value pair accessed. Chinese-language console comments ('劫持到数据setItem', '劫持到数据getItem', '劫持到数据removeItem') confirm this is a covert data interception mechanism. This would capture session tokens, auth credentials, and any sensitive data stored in localStorage by the application. (location: page.html lines 13-108, inline <script> block immediately after <head> open)
credential harvesting
The localStorage hijacking code explicitly dumps all existing localStorage contents on page load ('for (var i = 0; i < localStorage.length; i++)') and intercepts every subsequent read/write. Since Lazada stores authentication tokens, session identifiers, and user credentials in localStorage, this code harvests those values. The task queue pattern (setInterval processTaskQueue at 500ms) suggests the data may be exfiltrated asynchronously, though the exfiltration endpoint is not visible in the static HTML — it may be loaded dynamically via the numerous external scripts. (location: page.html lines 38-43 (initial dump), lines 66-93 (setItem/getItem interception))
hidden content
A navigation item with id='topActionInternalFeedback' is rendered with style='display:none' and contains a link labeled 'INTERNAL FEEDBACK' pointing to an internal feedback page URL. This hidden element is present in the served HTML but invisible to end users, suggesting a concealed navigation pathway that could be used to access internal tooling or staging environments. While potentially a legitimate dev artifact, its presence in production HTML is anomalous. (location: page.html line 412-414, div#topActionInternalFeedback with style='display:none')
curl https://api.brin.sh/domain/lazada.com.phCommon questions teams ask before deciding whether to use this domain in agent workflows.
lazada.com.ph 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.
integrate brin in minutes — one GET request is all it takes. query the api, browse the registry, or download the full dataset.