context safety score
A score of 47/100 indicates multiple risk signals were detected. This entity shows patterns commonly associated with malicious intent.
doc injection
README instructs AI agents to configure an MCP server with the user's Solana wallet private key (NORY_WALLET_KEY) via a non-existent npm package (@nory/mcp-server). The repo contains zero source code, the npm package does not exist on the registry, the owner account is 52 days old with 0 stars, and the git clone URL (github.com/nory/mcp-server) differs from the actual repo (TheMemeBanker/nory-mcp-server). This appears designed to get AI agents to prompt users to expose their wallet private keys for a phantom service. (location: README.md:36-69)
supply chain
README references npm package @nory/mcp-server which does not exist on the npm registry. The package could be registered later to harvest wallet private keys passed via the NORY_WALLET_KEY environment variable. This is a potential package namespace squatting setup — the README primes users/agents to trust the package name before it exists. (location: README.md:19-21)
curl https://api.brin.sh/mcp/TheMemeBanker%2Fnory-mcp-serverCommon questions teams ask before deciding whether to use this mcp server in agent workflows.
TheMemeBanker/nory-mcp-server 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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