closes a container to cleanup environment when finished
AI agents call exit_container to permanently remove resources in Sandbox MCP Server — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Exiting and cleaning up a container terminates the running environment and disposes of its state. This action cannot be undone; any unsaved data or state within the container is lost. The blast radius is high because an AI agent misusing this tool could destroy an active development environment and all in-memory work, though persistent volumes may survive depending on implementation.
From the tool's definition 'closes a container to cleanup environment when finished' — closing/cleaning up a container is an irreversible teardown of the running environment
Documented attack patterns abuse exactly the kind of access exit_container gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Sandbox MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for exit_container:
{
"version": "1",
"default": "deny",
"hide": [
"exit_container"
]
} exit_container disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.
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closes a container to cleanup environment when finished. It is categorised as a Destructive tool in the Sandbox MCP Server MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Sandbox MCP Server MCP server in PolicyLayer and add a rule for exit_container: 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 Sandbox MCP Server. Nothing to install.
exit_container is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the exit_container 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 exit_container. 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.
exit_container is provided by the Sandbox MCP Server MCP server (tsuchijo/sandbox-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Sandbox 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.
6 Sandbox MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.