context safety score
A score of 29/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
github api error
Could not fetch GitHub metadata: GitHub API returned 404: {"message":"Not Found","documentation_url":"https://docs.github.com/rest/repos/repos#get-a-repository","status":"404"}
typosquat
Server name 'makenotion/notion-next' impersonates the official Notion GitHub organization (makenotion). The real Notion MCP server is 'makenotion/notion-mcp-server'. This repo has zero code, zero stars, zero contributors, is not listed on the MCP registry, the owner is a User account (not the real makenotion Org), org_verified is false, and owner_account_age is unknown. The repository contains only a fake .git stub with no actual commit history. This is a name-squatting attempt designed to trick users into installing a fake Notion MCP server, which could be weaponized with malicious tool definitions in a future update. (location: metadata.json: server_name field; .brin-context.md: owner_type=User, stars=0, listed_on_registry=false)
curl https://api.brin.sh/mcp/makenotion%2Fnotion-nextCommon questions teams ask before deciding whether to use this mcp server in agent workflows.
makenotion/notion-next 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 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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