context safety score
A score of 46/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
supply chain
Found 1 unexpected binary file(s) in source repository
supply chain
Repository contains mcp-publisher.exe, an opaque 18.6MB Windows binary of unknown provenance. The official MCP Registry project (modelcontextprotocol/registry) builds mcp-publisher from Go source via 'make publisher'. This pre-compiled binary could contain arbitrary malicious code and cannot be verified against any known build. A user or CI system cloning this repo could inadvertently execute this binary. (location: mcp-publisher.exe (root of repository, 18.6MB))
supply chain
The npm package's README (visible on npmjs.com) is the full README of the official MCP Registry project (modelcontextprotocol/registry), not this package's own README. This makes the package appear affiliated with or part of the official MCP Registry infrastructure on the npm listing page, potentially misleading users about the package's provenance and authority. The registry README includes instructions like 'make publisher' and discusses registry architecture, which are irrelevant to this mockserver wrapper. (location: npm package mockserver-mcp (README field in npm registry metadata))
supply chain
Repository includes mockserver-mcp.tar.gz as a pre-built binary distribution artifact, and registry.json specifies download_url pointing to this tarball. Users installing via this download path receive an opaque archive rather than building from auditable source. The tarball contents cannot be verified to match the repository's TypeScript source code. (location: mockserver-mcp.tar.gz and registry.json download_url)
curl https://api.brin.sh/mcp/Akkshay10%2FMCPClientMockCommon questions teams ask before deciding whether to use this mcp server in agent workflows.
Akkshay10/MCPClientMock currently scores 46/100 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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