Check the TensorFeed AI/MCP/LLM supply-chain IOC feed. Returns publicly-disclosed malicious npm/PyPI packages whose name or summary signals relevance to AI agent operators. With no args, returns the whole snapshot (typically a small number of entries). With "package_name", returns only entries ma...
AI agents call check_ai_supply_chain_risk to retrieve information from TensorFeed without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
ecosystem | string | — | Optional ecosystem filter: "npm" or "pip" |
package_name | string | — | Optional case-insensitive substring of the package name (e.g. "mistralai" or "@mistralai/mistralai-gcp"). If omitted, returns all current entries. |
Parameters from the server's own tool schema.
This tool retrieves and queries existing public security advisory data to help agents assess package risk before installation. It has no side effects, cannot modify data, and does not execute code or trigger external operations.
From the tool's definition Tool description states it 'Returns publicly-disclosed malicious npm/PyPI packages' and 'TF republishes already-public advisories'. The verbs are 'Check' and 'Returns' — pure data retrieval with no modification, deletion, or code execution.
Attacks that exploit this kind of access
Check the TensorFeed AI/MCP/LLM supply-chain IOC feed. Returns publicly-disclosed malicious npm/PyPI packages whose name or summary signals relevance to AI agent operators. With no args, returns the whole snapshot (typically a small number of entries). With "package_name", returns only entries matching that name (substring, case-insensitive) so an agent can ask "is X risky right now?" before installing. Each entry cites its GHSA primary source. Posture: TF republishes already-public advisories; the listed primary source is authoritative. It is categorised as a Read tool in the TensorFeed MCP Server, which means it retrieves data without modifying state.
check_ai_supply_chain_risk accepts 2 parameters: ecosystem, package_name. The full parameter table on this page comes from the server's own tool schema.
Register the TensorFeed MCP server in PolicyLayer and add a rule for check_ai_supply_chain_risk: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches TensorFeed. Nothing to install.
check_ai_supply_chain_risk is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the check_ai_supply_chain_risk rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for check_ai_supply_chain_risk. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
check_ai_supply_chain_risk is provided by the TensorFeed MCP server (https://mcp.tensorfeed.ai/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
Teams ship this data inside their own products. See what a licence covers →