Reboot a virtual machine.
AI agents invoke reboot_vm to trigger actions in Cloud VM MCP. 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.
Rebooting a VM triggers an external operational effect (restarting the machine), causing service interruption and downtime. It is not purely destructive (no data loss) but executes a disruptive control action on infrastructure. Misuse could cause significant availability impact across production workloads.
From the tool's definition Reboot a virtual machine
Attacks that exploit this kind of access
Reboot a virtual machine. It is categorised as a Execute tool in the Cloud VM MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Cloud VM MCP server in PolicyLayer and add a rule for reboot_vm: 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 Cloud VM MCP. Nothing to install.
reboot_vm 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 reboot_vm 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 reboot_vm. 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.
reboot_vm is provided by the Cloud VM MCP server (sondt2709/cloud-vm-mcp-py). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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