context safety score
A score of 39/100 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
obfuscated code
URL-encoded JavaScript is decoded and executed at runtime using eval(decodeURIComponent(...)). The decoded function sets window.jsToken to a hardcoded token value ('66A1AD0F19A1F1F04FCD999CE804F7D914EAAE357EC52DD56B25F66FEDE5D605786BF80F7ADAB7B668F66CC6C79D9EE7C50B6ECB31E9F9A1D3D945367A54BD0B9DB7E48004FDEA4CBA32D5EC52258F3976D53286D8DB599F53F4A52978D955BF'). The use of eval with URL-encoded payloads is a classic obfuscation technique used to evade static analysis. (location: page-text.txt:1 — eval(decodeURIComponent(`function%20fn%28a%29...`)))
obfuscated code
A script tag is dynamically injected into the DOM at runtime using document.createElement('script') with a cache-busting random query parameter (?v=Math.random()). This pattern is used to load external scripts in a way that bypasses static URL scanning and CDN caching controls. (location: page.html:49-53 / page-text.txt:49-53 — dynamic script injection of abclite-2068-s.js)
credential harvesting
The page integrates multiple third-party OAuth login SDKs (Apple, Facebook, Google GSI, Kakao, LINE LIFF), all loaded asynchronously from their respective CDNs. While individually legitimate, aggregating credentials across five identity providers on a cloud storage platform operated by a Chinese company (Flextech Inc./Baidu-affiliated) increases the risk surface for credential interception or misuse, especially given the use of obfuscated inline JS on the same page. (location: page.html lines 1 (script tags): apple.min.js, facebook.min.js, kakao.min.js, accounts.google.com/gsi/client, static.line-scdn.net/liff/edge/2/sdk.js)
curl https://api.brin.sh/domain/terabox.comCommon questions teams ask before deciding whether to use this domain in agent workflows.
terabox.com currently scores 39/100 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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