execute_batch
AI agents invoke execute_batch to trigger actions in Ai Mcp Terminal. 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 executes arbitrary commands in batch across multiple terminal sessions. Batch execution on 100 concurrent terminals amplifies blast radius significantly—a malicious agent could compromise multiple systems, exfiltrate data, deploy malware, or pivot through a network in a single batch operation. The Execute category applies because effects are argument-dependent and uncontrolled.
From the tool's definition Tool is on 'ai-mcp-terminal' server described as enabling 'async command execution' and 'batch operations' across 'up to 100 concurrent terminals.' Sibling tools include 'execute_command', 'execute_sequence', 'execute_with_retry', and 'broadcast_command', all…
Attacks that exploit this kind of access
execute_batch. It is categorised as a Execute tool in the Ai Mcp Terminal MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Ai Mcp Terminal MCP server in PolicyLayer and add a rule for execute_batch: 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 Ai Mcp Terminal. Nothing to install.
execute_batch 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 execute_batch 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 execute_batch. 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.
execute_batch is provided by the Ai Mcp Terminal MCP server (kanniganfan/ai-mcp-terminal). 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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