delete_run

THE RISK

Critical Risk

Why delete_run needs a policy

This tool performs an irreversible deletion operation that removes data from normal access paths. Although MLflow marks it as recoverable rather than permanently purged, deletion that hides data from UI/queries and requires special API access to recover constitutes a destructive action.

From the tool's definition Tool name 'delete_run' combined with description stating it 'Delete[s] a run' and 'Moves it to the deleted lifecycle stage — not shown in UI or queries'.

Documented attack patterns abuse exactly the kind of access delete_run gives an agent:

POLICY

How to control delete_run

PolicyLayer is an MCP gateway — it sits between your AI agents and MLflow MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for delete_run:

policy.json

{
  "version": "1",
  "default": "deny",
  "hide": [
    "delete_run"
  ]
}

delete_run disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.

Create a free account and register MLflow MCP Server — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

RESTRICT THIS TOOL →

Free to start. No card required.

EXPLORE

Related tools and policies

More MLflow MCP Server tools

Destructive delete_experiment Delete an experiment and all its runs. Moves to the 'deleted' lifecycle stage — not shown Destructive delete_model_alias Remove an alias from a registered model (e.g. revoke 'champion'). The alias is permanently Destructive delete_model_version Delete a specific model version from the registry. Irreversible — the version and its meta Destructive delete_registered_model Delete an entire registered model and all its versions. Irreversible — all versions, alias Write transition_model_version_stage transition_model_version_stage Write copy_model_version copy_model_version Write register_model register_model Write set_experiment_tag Set a tag on an experiment.

All 40 MLflow MCP Server tools →

Destructive tools on other servers

Android Forensics ADB MCP Server create_full_backup 0xarchive web3_revoke_key Frida Game Hacking MCP clear_scan cPanel MCP Server clear_spam_box

Go deeper

Critical-risk tools in MLflow MCP Server → Every critical-risk tool this server exposes, in one view.
All critical-risk MCP tools → Every critical-risk tool across the catalogue, and the policy pattern that contains them.
The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Blast radius → How much damage one compromised or confused agent can do — and how to bound it.
Tool call approval → Approval gates on individual tool calls: auto-allow, prompt, or deny.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about delete_run

What does the delete_run tool do? +

Delete a run. Moves it to the 'deleted' lifecycle stage — not shown in UI or queries, but recoverable via the MLflow API. It is categorised as a Destructive tool in the MLflow MCP Server MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.

How do I enforce a policy on delete_run? +

Register the MLflow MCP Server MCP server in PolicyLayer and add a rule for delete_run: 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 MLflow MCP Server. Nothing to install.

What risk level is delete_run? +

delete_run is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.

Can I rate-limit delete_run? +

Yes. Add a rate_limit block to the delete_run 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.

How do I block delete_run completely? +

Set action: deny in the PolicyLayer policy for delete_run. 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.

What MCP server provides delete_run? +

delete_run is provided by the MLflow MCP Server MCP server (kkruglik/mlflow-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every MLflow MCP Server tool call.

Start from MLflow MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

CHECK YOUR STACK →