AI agents use train_node_regression_model to create or update resources in Neo4j Gds — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Neo4j Gds environment.
This tool trains a machine learning model and persists it to the GDS model catalog. It creates/writes a new artifact (the trained model) but does not delete data or execute arbitrary code. The blast radius is medium — a misconfigured model could consume significant compute resources and pollute the model catalog, but the action is reversible by removing the stored model.
From the tool's definition 'Train a node regression model...and store it in the GDS model catalog under modelName'
Attacks that exploit this kind of access
Train a node regression model on a projected graph and store it in the GDS model catalog under modelName. It is categorised as a Write tool in the Neo4j Gds MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Neo4j Gds MCP server in PolicyLayer and add a rule for train_node_regression_model: 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 Neo4j Gds. Nothing to install.
train_node_regression_model is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the train_node_regression_model 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 train_node_regression_model. 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.
train_node_regression_model is provided by the Neo4j Gds MCP server (neo4j-contrib/gds-agent). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
train_node_regression_model is one line of Neo4j Gds's registry record.
The record carries the whole server: verified identity, auth posture, risk grade, every tool classified, recommended policy — re-checked continuously.
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