k8s_rollout_resume

THE RISK

High Risk

Why k8s_rollout_resume needs a policy

Resuming a rollout triggers an external Kubernetes operation that deploys new pod versions across a workload. This can affect running services and application availability at scale, making it an Execute action with high blast radius if misused (e.g., resuming a bad rollout causing widespread pod failures).

From the tool's definition Resume a previously paused rollout for a deployment, daemonset, or statefulset

Documented attack patterns abuse exactly the kind of access k8s_rollout_resume gives an agent:

POLICY

How to control k8s_rollout_resume

PolicyLayer is an MCP gateway — it sits between your AI agents and Multi Cluster Kubernetes MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for k8s_rollout_resume:

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "k8s_rollout_resume": {
      "limits": [
        {
          "counter": "k8s_rollout_resume_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

k8s_rollout_resume stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.

Create a free account and register Multi Cluster Kubernetes 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.

RATE-LIMIT THIS TOOL →

Free to start. No card required.

EXPLORE

Related tools and policies

More Multi Cluster Kubernetes MCP Server tools

Destructive k8s_delete_ns Delete a Kubernetes namespace and all resources within it. Destructive k8s_delete_resource Delete a Kubernetes resource. Supports all common resource types Destructive k8s_drain_node Drain a node by evicting all pods, preparing it for maintenance. Execute k8s_pod_exec Execute a command inside a running container. Supports quoted arguments Execute k8s_rollout_restart Trigger a rolling restart of a deployment, daemonset, or statefulset. Execute k8s_run_pod Create and run a pod with the specified container image. Execute k8s_scale_resource Scale a deployment, statefulset, or replicaset to the specified number of replicas. Execute k8s_rollout_pause Pause an in-progress rollout for a deployment, daemonset, or statefulset.

All 57 Multi Cluster Kubernetes MCP Server tools →

Execute tools on other servers

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Go deeper

High-risk tools in Multi Cluster Kubernetes MCP Server → Every high-risk tool this server exposes, in one view.
All high-risk MCP tools → Every high-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.
Agent sandbox → Bounding what an agent can execute and where the boundary should sit.
Least privilege for MCP → Granting agents the minimum tool surface the task needs.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about k8s_rollout_resume

What does the k8s_rollout_resume tool do? +

Resume a previously paused rollout for a deployment, daemonset, or statefulset. It is categorised as a Execute tool in the Multi Cluster Kubernetes MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on k8s_rollout_resume? +

Register the Multi Cluster Kubernetes MCP Server MCP server in PolicyLayer and add a rule for k8s_rollout_resume: 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 Multi Cluster Kubernetes MCP Server. Nothing to install.

What risk level is k8s_rollout_resume? +

k8s_rollout_resume is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit k8s_rollout_resume? +

Yes. Add a rate_limit block to the k8s_rollout_resume 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 k8s_rollout_resume completely? +

Set action: deny in the PolicyLayer policy for k8s_rollout_resume. 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 k8s_rollout_resume? +

k8s_rollout_resume is provided by the Multi Cluster Kubernetes MCP Server MCP server (razvanmacovei/k8s-multicluster-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Multi Cluster Kubernetes MCP Server tool call.

Start from Multi Cluster Kubernetes 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 →