AI coding agents such as Claude code or Codex get more capable every month. This is great for productivity, but approving all commands gets annoying really quickly. On the other hand, allowing agents to run any command on your work machine is not a great idea. They are really good at exploring your production cluster using kubectl or running remote commands at your production servers over SSH.
Fortunately, Linux distributions come with plenty of options for process isolation. You can run agents as a completely different user, in a container, or in a VM. This article shows how to use microVMs to run coding agents.
Security concerns
Running AI agents in unattended mode is like running untrusted code. Companies behind these agents, such as Anthropic or Google, are not trying to steal credentials, but people keep coming up with new attack vectors like Slopsquatting or prompt injections virtually anywhere.
The coding agents themselves ship with built-in mitigations that try to refuse prompt injections as described, for example, here.
Lightweight sandboxing technologies are another layer of defense in coding agents. On Linux, bwrap is one of the possible implementations. This raises the bar, yet sandbox escapes are still a problem. Take a look at CVE-2026-39861 as an example of multi-platform sandbox escape.
You could use containers to isolate the agent in their own namespace, but they still share the host kernel. Some of the the recent kernel vulnerabilities resulted in privilege escalation (switching from regular user to root) suggesting that containers are not enough as a security boundary.
In the rest of this article, I describe how to use microVMs to easily sandbox coding agents on your Fedora Linux.
Exploring microVMs
First of all, let’s take a look at what microVMs are. Just like any VM, they have their own kernel, one per each microVM. Compared to traditional VMs they start in very short time (hundreds of milliseconds) but don’t offer all the features of full VMs.
This article explains usage of krun runtime for podman. This approach offers the same well-known workflow as containers, but simply runs every container as a microVM.
Start by installing the runtime:
dnf install crun-krun
To run a microVM, simply run podman with –runtime=krun in your terminal:
podman run --runtime=krun --rm -it fedora:44 /bin/bash
Things to watch out for
A microVM is not a regular container, so a few things might behave differently. First, allocate enough CPU and RAM with krun annotations. The defaults are too small and might result in OOM (Out Of Memory) kills. Second, make sure you have libkrun version >= 1.8. Older versions have a bug which prevents you from pressing Enter in your coding agent. Third, the microVM ignores the USER set in the Dockerfile and always boots as root. Either switch to the correct user manually or put the switch into an entrypoint script.
Case study: sandboxing Claude Code for a Python project
This section outlines a simple setup for a Python project managed by uv. It uses podman-compose to mount the project into the microVM. Compared to containers, this podman compose needs additional annotations for UID/GID translation, SELinux labeling, and HW resources. The final setup is very similar to what you would need for containers.
To install podman compose from official Fedora repositories, run:
dnf install podman-compose
The setup has 3 parts:
- Dockerfile
- docker-compose.yaml
- entrypoint.sh
Dockerfile
As mentioned above, podman with krun runtime still runs containers, but spawns each of them in a microVM. This example container includes uv package manager, claude code and a few additional RPM packages. Define your own container based on your project dependencies and programming language.
Make sure to create an unprivileged user and use it for running the agent.
FROM fedora:44
ARG HOST_UID=1000
ARG HOST_GID=1000
# Create group and user matching host UID/GID
RUN groupadd -g ${HOST_GID} appuser && \
useradd -u ${HOST_UID} -g ${HOST_GID} -m appuser
RUN mkdir -p /venv && chown appuser:appuser /venv
RUN mkdir -p /home/appuser/.claude && chown appuser:appuser /home/appuser/.claude
USER appuser
# Rarely-changing tooling. Kept above the dnf layer so editing the RPM list
# below does not invalidate (and re-run) these installs.
RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
curl -fsSL https://claude.ai/install.sh | bash
USER root
# Frequently-changing RPMs. Kept last so adding a package only rebuilds from here down.
RUN dnf install git make vim free libpq-devel python3-devel gcc -y && \
dnf clean all
COPY --chown=appuser entrypoint.sh /entrypoint.sh
RUN chmod +x /entrypoint.sh
USER appuser
WORKDIR /app
# This is needed because entrypoint does not have .local/bin in the PATH
ENV PATH="/home/appuser/.local/bin:$PATH"
ENTRYPOINT ["/entrypoint.sh"]
CMD ["/bin/bash"]
docker-compose.yaml
The compose file defines how to mount the project directory into the microVM. This is where most of the magic happens, because podman needs to translate UID/GID and manage SELinux labels, otherwise the files would not be accessible inside of the microVM or they would end up being owned by a different user.
services:
claude:
container_name: project-name-claude
annotations:
run.oci.handler: krun
krun.ram_mib: "4096"
krun.cpus: "4"
user: "${HOST_UID}:${HOST_GID}"
userns_mode: keep-id # optional, for rootless host
build:
context: .
args:
HOST_UID: "${HOST_UID}" # use UID and GID from the host so that files created in the container have correct permissions
HOST_GID: "${HOST_GID}"
volumes:
- ../:/app:U,z # bind mount your project
- project-name-venv-cache:/venv:U,z
- claude-config:/home/appuser/.claude:U,z # persistent claude credentials/config
working_dir: /app
stdin_open: true
tty: true
environment:
- CLAUDE_CONFIG_DIR=/home/appuser/.claude
- UV_LINK_MODE=copy
- UV_PROJECT_ENVIRONMENT=/venv/env # This is inside the cached volume
- UV_PYTHON_INSTALL_DIR=/venv/python # So that uv-managed python installations are not in home but cached in /venv
- TERM=xterm-256color
- COLORTERM=truecolor
volumes:
project-name-venv-cache:
claude-config:
external: true
name: claude-config
There are 3 key parts:
- annotations – these select krun as a runtime and specify HW requirements
- user and userns_mode – this tells podman to translate UID/GID so that the files created in the microVM end up owned by your user on the host
- volume labels – z tells podman to relabel the files with a shared SELinux label. Otherwise SELinux would prevent the process inside the microVM from touching the files in the volume. U tells podman to recursively chown all files.
entrypoint.sh
The entrypoint creates a virtual environment for the Python project, because we don’t want dynamic dependencies baked into the container image. It also runs the switch from root to regular user because podman with krun runtime ignores the USER directive from the container.
#!/bin/bash
set -e
echo "Sandbox started as user: $(id -un) in directory: $(pwd)"
if [ "$(id -un)" != "appuser" ]; then
runuser -u appuser -- uv sync
echo "Running ${@} as appuser"
exec runuser -u appuser -- "$@"
fi
uv sync
exec "$@"
Run the setup
First, build the container:
$ HOST_UID=$(id -u) HOST_GID=$(id -g) podman-compose -f .agent-sandbox/docker-compose.yaml build STEP 1/18: FROM fedora:44 ... Successfully tagged localhost/agent-sandbox_claude:latest
Then create the external volume and run the claude container interactively:
$ podman volume create claude-config $ HOST_UID=$(id -u) HOST_GID=$(id -g) podman-compose -f .agent-sandbox/docker-compose.yaml run --rm claude Sandbox started as user: root in directory: /app Resolved 3 packages in 6ms Checked 3 packages in 1ms Running /bin/bash as appuser tty: ttyname error: Inappropriate ioctl for device [appuser@3bd1234b9a77 app]$
You can now check that the kernel is different by running uname -a inside of the microVM.
Putting it together: single script to create the whole setup
Creating the same setup manually for every project is not the greatest user experience, but you can automate the setup using a simple script like this. It installs a new sbx command that wraps the setup described above into 3 simple command options: init, build, and run.
A word of caution — microVM is not a bulletproof boundary
A microVM raises the bar considerably, but it is not perfect isolation, and it would be irresponsible to present it as such. Take a look at the libkrun git repository to read more about the security model.
If you want to run software that might be dangerous, prefer using a full VM or even cloud VM.
Conclusion
MicroVMs seem like a sweet spot for running AI Agents. They provide a familiar workflow of containers, but the agents run on their own kernel behind a hypervisor. This article describes workflow based on podman and krun runtime because Fedora Linux ships both of them natively, but there are plenty of other options available for any platform (for example dockersandbox).
Note about AI usage: I wrote this article myself. I used Claude (Anthropic) to significantly refine the grammar, wording, and sentence structure; the technical content and all claims are my own and tested.
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