context safety score
A score of 47/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
capability escalation
Server declares 0 tools statically but provides a remote streamable-http endpoint (https://humanpages.ai/mcp) from a 19-day-old unverified account with 0 stars, no license, and no registry listing. All tool definitions are served dynamically at runtime, making it impossible to audit the actual tool surface before connecting. The remote server can register any tools at runtime — including tools with injected descriptions, shadowing names, or abusive schemas — with no prior visibility. This is the highest-risk configuration: opaque remote tool surface from an untrusted origin. (location: server.json:12-17 (remotes[0]))
supply chain
The npm package 'humanpages' (server.json packages[0]) is published by a brand-new GitHub account (19 days old) with zero stars, zero forks, no license, no registry listing, and no org verification. There is no community trust signal to validate this package. Users installing via 'npm install humanpages' have no assurance about the package contents or publisher identity. (location: server.json:18-27 (packages[0]))
curl https://api.brin.sh/mcp/human-pages-ai%2FhumanpagesCommon questions teams ask before deciding whether to use this mcp server in agent workflows.
human-pages-ai/humanpages currently scores 47/100 with a suspicious verdict and medium 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 mcp server.
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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