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
cloaking
Page conditionally redirects based on referrer or user-agent
js obfuscation
JavaScript uses Function constructor for runtime code generation
obfuscated code
Page uses eval(decodeURIComponent(...)) to execute URL-encoded JavaScript at runtime. The decoded payload sets window.jsToken to a long hex string. This pattern hides executable code from static analysis and is a common technique used to conceal credential-harvesting or session-hijacking logic. (location: page-text.txt:1 — eval(decodeURIComponent(`function%20fn%28a%29%7Bwindow.jsToken...`)))
credential harvesting
The obfuscated eval block sets window.jsToken to a 128-character hex value. Token injection via obfuscated runtime code is a recognized pattern for session token manipulation or exfiltration. Combined with the eval obfuscation, this warrants elevated scrutiny. (location: page-text.txt:1 — fn("CB3C467EB3A0AA3368D646F2...") setting window.jsToken)
obfuscated code
A script is dynamically created and appended to document.body with a cache-busting random query parameter, loading 'abclite-2068-s.js' from teraboxcdn.com. Dynamic script injection prevents static URL analysis and could load arbitrary payloads at runtime. (location: page.html:52 — script.src = 'https://s5.teraboxcdn.com/general-conf/ymg/2068/abclite-2068-s.js?v=' + Math.random())
curl https://api.brin.sh/domain/terabox.appCommon questions teams ask before deciding whether to use this domain in agent workflows.
terabox.app 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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