Run inference using a trained model artifact or artifact_id. Use artifact_id for fast predictions right after training (valid for 5 minutes).
Part of the WarpGBM server.
Free to start. No card required.
AI agents invoke predict_from_artifact to trigger processes or run actions in WarpGBM. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
predict_from_artifact can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"predict_from_artifact": {
"limits": [
{
"counter": "predict_from_artifact_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full WarpGBM policy for all 6 tools.
These attack patterns abuse exactly the kind of access predict_from_artifact gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Run inference using a trained model artifact or artifact_id. Use artifact_id for fast predictions right after training (valid for 5 minutes).. It is categorised as a Execute tool in the WarpGBM MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the WarpGBM MCP server in PolicyLayer and add a rule for predict_from_artifact: 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 WarpGBM. Nothing to install.
predict_from_artifact is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the predict_from_artifact 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 predict_from_artifact. 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.
predict_from_artifact is provided by the WarpGBM MCP server (jefferythewind/warpgbm-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 6 WarpGBM tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.