Roll back a deployment, daemonset, or statefulset to a previous revision.
AI agents invoke k8s_rollout_undo to trigger actions in Multi Cluster Kubernetes MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool triggers a rollback operation on Kubernetes workloads, reverting them to a previous revision. This is an active execution that changes the running state of cluster workloads.
From the tool's definition Roll back a deployment, daemonset, or statefulset to a previous revision
Documented attack patterns abuse exactly the kind of access k8s_rollout_undo gives an agent:
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_undo:
{
"version": "1",
"default": "deny",
"tools": {
"k8s_rollout_undo": {
"limits": [
{
"counter": "k8s_rollout_undo_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
} k8s_rollout_undo 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.
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Roll back a deployment, daemonset, or statefulset to a previous revision. 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.
Register the Multi Cluster Kubernetes MCP Server MCP server in PolicyLayer and add a rule for k8s_rollout_undo: 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.
k8s_rollout_undo 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 k8s_rollout_undo 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 k8s_rollout_undo. 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.
k8s_rollout_undo 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.
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.
Free to start. No card required.
57 Multi Cluster Kubernetes MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.