context safety score
A score of 38/100 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
Publisher name 'checkra1neth' closely mimics 'checkra1n', a well-known iOS jailbreak project. Combined with zero trust signals (0 stars, 0 forks, no license, not on MCP registry, unverified account, unknown account age) and a now-deleted GitHub repository (404), this pattern is consistent with brand impersonation. The repository contains no source code or tool definitions, suggesting it was either a placeholder for future malicious content or was already removed. (location: metadata.json: server_name field 'checkra1neth/xbird'; GitHub account 'checkra1neth')
supply chain
The MCP server repository is completely empty — zero tools, zero source files, no package registry presence, no version, and the GitHub repo returns 404. While currently inert, an empty or deleted repo claimed as an MCP server with a suspicious publisher name could be repopulated with malicious tools at any time, or could be used as a social engineering lure. (location: metadata.json: tool_count=0, stars=0, package_registries=[], transport_types=[])
curl https://api.brin.sh/mcp/checkra1neth%2FxbirdCommon questions teams ask before deciding whether to use this mcp server in agent workflows.
checkra1neth/xbird currently scores 38/100 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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