AI agents invoke run_custom_command to trigger actions in Kali Linux 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.
A tool that executes custom commands in a privileged Docker container has the highest blast radius: arbitrary code execution with root or elevated privileges in an isolated but fully operational Linux environment. This allows an AI agent to run any shell command, modify system state, exfiltrate data, or chain attacks.
From the tool's definition Tool name 'run_custom_command' with empty description in a Kali Linux MCP server context that 'enabling network scanning (nmap), web vulnerability scanning (nikto), and custom command execution.' The server description explicitly mentions 'custom command…
Documented attack patterns abuse exactly the kind of access run_custom_command gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Kali Linux MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for run_custom_command:
{
"version": "1",
"default": "deny",
"tools": {
"run_custom_command": {
"limits": [
{
"counter": "run_custom_command_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
} run_custom_command 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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run_custom_command. It is categorised as a Execute tool in the Kali Linux MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Kali Linux MCP Server MCP server in PolicyLayer and add a rule for run_custom_command: 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 Kali Linux MCP Server. Nothing to install.
run_custom_command 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 run_custom_command 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 run_custom_command. 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.
run_custom_command is provided by the Kali Linux MCP Server MCP server (marklechner/kali-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Kali Linux MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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7 Kali Linux MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.