context safety score
A score of 29/100 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
cloaking
Page conditionally redirects based on referrer or user-agent
js obfuscation
JavaScript uses Function constructor for runtime code generation
brand impersonation
The domain 1024tera.com hosts a page branded as 'TeraBox' with title 'TeraBox - Free Cloud Storage Up To 1 TB, Send Large Files Online', but the actual domain (1024tera.com) does not match the official TeraBox brand domain. The canonical URL and og:url both point to www.1024tera.com while the brand name TeraBox implies a different origin. This mismatch is a classic brand impersonation pattern where a non-brand domain serves content under a well-known brand identity. (location: page.html:1 - <title>, og:title, og:site_name, canonical link)
obfuscated code
The page executes URL-encoded (percent-encoded) JavaScript via eval(decodeURIComponent(...)). The decoded payload sets window.jsToken to a long hex string. Using eval on encoded strings is a well-known obfuscation technique to hide malicious logic from static analysis and security scanners. The token value 'FC400CA30763762A01A472381A3E2C01BD2F4AAB1F632B7E3C1132AE97048C8C...' is injected into the global window scope, potentially for credential or session exfiltration. (location: page-text.txt:1 - eval(decodeURIComponent(`function%20fn%28a%29%7Bwindow.jsToken...`)))
credential harvesting
The page loads third-party authentication SDKs from Apple, Facebook, Google (accounts.google.com/gsi/client), Kakao, and LINE, while operating under a non-brand domain (1024tera.com). If users authenticate via these OAuth flows on a site impersonating TeraBox, their OAuth tokens or credentials may be harvested by the site operator rather than the legitimate TeraBox service. (location: page.html - apple.min.js, facebook.min.js, kakao.min.js, accounts.google.com/gsi/client, static.line-scdn.net)
obfuscated code
A dynamically injected script is loaded from 'https://s5.teraboxcdn.com/general-conf/ymg/2068/abclite-2068-s.js' with a random cache-busting query parameter (?v=Math.random()). The script is appended to the DOM at runtime, bypassing static CSP analysis. The 'ymg' path component and random versioning are common patterns used in evasive ad-fraud and tracking scripts. (location: page.html:49-53 / page-text.txt:49-53 - document.createElement('script') with random ?v= parameter)
prompt injection
The templateData object embedded in the page exposes internal configuration including pcftoken, CDN origin, region domain prefix, and userVipIdentity fields in plaintext. If an AI agent were to parse or summarize this page, these tokens could be interpreted as instructions or used to manipulate agent state (e.g., setting window.jsToken globally via eval). The combination of eval-executed code and globally scoped token injection could affect AI browser-automation agents operating on the page. (location: page-text.txt:1 - var templateData = {...}; eval(decodeURIComponent(...)))
curl https://api.brin.sh/domain/1024tera.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
1024tera.com currently scores 29/100 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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