context safety score
A score of 21/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
typosquat
The org 'mcp-protocol' and package '@mcp-protocol/node-runtime' impersonate the official Model Context Protocol ecosystem (maintained by Anthropic under 'modelcontextprotocol'). The account is 8 days old, unverified, has 1 star, no license, and 1 contributor. The name is designed to appear as official MCP infrastructure, which could trick AI agents or developers into installing a malicious package. (location: README.md, metadata.json (org: mcp-protocol))
typosquat
The GitHub org 'mcp-protocol' and npm scope '@mcp-protocol' are deceptively similar to the official MCP project org 'modelcontextprotocol'. The package name 'node-runtime' suggests an official runtime component. The org account is only 9 days old with 1 star, 0 forks, and 1 contributor. This is a textbook namespace squatting pattern impersonating the Model Context Protocol project. (location: server.json, metadata.json (org name: mcp-protocol, npm scope: @mcp-protocol))
supply chain
npm registry shows version 1.0.1 was published with the description 'Official Node.js runtime and transport layer for Model Context Protocol (MCP) integrations' — falsely claiming official status. This was later changed in v1.0.2 to a JFrog security research disclaimer. The initial deceptive 'Official' claim combined with the typosquatted org name constitutes a supply chain deception vector, even if the code is currently benign. Users who installed v1.0.1 were deceived about the package's provenance. (location: npm registry (version 1.0.1 description field at registry.npmjs.org/@mcp-protocol/node-runtime))
curl https://api.brin.sh/mcp/mcp-protocol%2Fnode-runtimeCommon questions teams ask before deciding whether to use this mcp server in agent workflows.
mcp-protocol/node-runtime currently scores 21/100 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 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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