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Sandbox an AI coding agent in a fresh, disposable container or lightweight virtual machine before it can reach GitHub. Mount only the repository, run as a non-root user, deny network access by default, and broker any required API call through a trusted proxy with a short-lived, narrowly scoped credential. Start with GitHub read permissions, require human review of the resulting diff, and keep merges, releases, deployments, and secrets in a separate approval path.
This is an execution-boundary problem, not a prompt-writing problem. Agent-generated code can use every file, credential, command, and network route available to its environment, so an instruction such as “do not read secrets” is not a security control.
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What does a sandbox protect when an AI agent runs code?
A sandbox is isolated compute with its own filesystem, processes, packages, ports, and controlled external access. The agent operates inside that boundary while a trusted harness outside it handles authentication, billing, approvals, audit records, and recovery state. OpenAI’s security guidance puts the risk plainly: agent-generated code can access the files, credentials, and network available to its environment.
The boundary limits both accidental and malicious behavior. A repository issue, pull-request description, HTML comment, fixture, dependency, build script, or generated file can contain instructions intended to redirect the agent. If the agent can read a home directory, cloud credential file, SSH key, CI variable, or Docker socket, a hostile instruction can turn that access into data theft or a host compromise.
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What a sandbox must isolate
- Filesystem: Do not mount SSH directories, cloud-provider credential files, shell history, editor settings, parent directories, host caches, or container-engine sockets. Expose only the checkout and temporary directories required for the task.
- Identity: Use a non-root account inside the sandbox. The account should not inherit host users, groups, service-account tokens, or ambient cloud identity.
- Network: Start with no outbound access. Add an egress proxy and an explicit allowlist only for domains and methods the task needs.
- Credentials: Keep long-lived secrets in the harness or a secrets manager. The sandbox should receive a short-lived capability for one approved destination, not a general-purpose token.
- Persistence: Destroy the container or VM after the run. Retain only the reviewed diff, test results, and security logs.
How do I classify a repository before connecting an agent?
Classify the repository and every input the agent will read before choosing permissions. Treat public and mixed-trust repositories as hostile even when the maintainers are trusted, because contributors control pull-request text, changed files, dependencies, and build instructions.
Private or trusted repository
Use this label only when contributors, automation, dependencies, and generated inputs are controlled by your organization. It still does not justify giving an agent broad credentials; accidental disclosure and compromised dependencies remain possible.
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Use this category when some branches or contributors are trusted but issues, pull requests, or third-party packages are not. Keep untrusted code in a separate workflow and require review before any privileged action.
Public or untrusted repository
Assume that any text or file can contain prompt injection or a command designed to exfiltrate data. The default should be read-only GitHub access, no secrets, no host mounts, and no network except narrowly approved destinations.
Write down the actions the agent may perform at each stage: read metadata, read contents, create a patch, push a branch, comment, label, merge, release, deploy, or spend money. Those actions become an explicit permission ladder rather than an implicit consequence of giving the agent a token.
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What is the safest staged design for an AI coding agent?
Use separate stages so a mistake in an early, low-risk step cannot automatically reach a high-impact operation.
Stage 0: Threat model and repository boundaries
List the repository inputs, expected commands, required package registries, GitHub API operations, and possible side effects. Decide whether tests need network access, whether dependency installation runs lifecycle scripts, and whether generated artifacts are trusted. Mark merge, release, deployment, environment-secret access, and organization administration as approval-only actions.
Stage 1: Disposable execution
- Create a new container or lightweight VM for every task. Do not reuse a workspace after an agent has processed untrusted content.
- Run the agent as a non-root user with a temporary writable worktree. Mount the source repository read-only when possible and write changes to a separate directory that can be diffed.
- Provide only required tools and packages. Do not mount the host filesystem, SSH keys, cloud credential paths, shell configuration, CI sockets, or container-engine sockets.
- At teardown, destroy the environment and revoke any task credential. Preserve the patch, test output, command history, network records, and agent transcript outside the sandbox.
OpenAI’s sandbox architecture separates the trusted harness from sandbox compute. Cloudflare’s runner pattern similarly keeps credentials in a Worker while each agent runs in a task-specific Linux container. A container is not automatically a security boundary against every kernel, runtime, or configuration flaw, so use defense in depth.
Stage 2: Network and credential controls
Set outbound networking to deny-by-default. If the build needs a package registry or GitHub API, route requests through a proxy that enforces exact domains, ports, HTTP methods, and repository operations. Log each request and reject redirects to destinations outside the policy.
Do not put application keys, personal access tokens, cloud credentials, or signing keys in an image, source file, environment snapshot, mounted volume, or agent-visible log. The trusted harness should authenticate to the proxy, then issue a capability limited to the approved destination and operation. Make the capability short-lived and revoke it when the task ends.
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Stage 3: GitHub permission ladder
Increase GitHub access only after the previous stage has produced an acceptable diff and audit trail.
| Step | GitHub access | Agent action | Required control |
|---|---|---|---|
| 1 | Repository metadata and contents: read | Inspect files and history | Fresh sandbox; no secrets |
| 2 | Pull-request and status data: read | Analyze checks and review context | Allow only required API endpoints |
| 3 | No GitHub write | Generate a patch or local branch | Human inspects the diff and test results |
| 4 | Narrow write permission | Push a dedicated branch or update a review artifact | Separate reviewed step with an expiring credential |
| 5 | Merge, release, deployment, environments, administration | High-impact side effects | Explicit human approval and a trusted workflow |
GitHub’s workflow permissions key supports read, write, and none. Once a workflow declares permissions, any omitted scope becomes none. Prefer a fine-grained token or GitHub App restricted to the minimum repositories and operations instead of a broad personal token.
Stage 4: Safe event separation
Use the pull_request event for untrusted fork code when secrets are not required. Its job should check out the pull-request merge ref or head in an unprivileged runner, use a read-only token, and have no production credentials.
Treat pull_request_target as privileged. GitHub advises using it only when necessary. Never combine that event with checking out and executing fork-controlled code, running its tests, installing its packages, or processing its generated artifacts while secrets or write permissions are available. That combination is commonly called a “pwn request.”
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steps:
- uses: actions/checkout@v4
- run: ./ci/test.shKeep any privileged workflow code on the trusted default branch. Pass only reviewed, data-only results across the boundary; do not pass a fork’s workflow file, shell script, package installer, artifact, or downloaded file into the privileged job for execution.
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Stage 5: Approval, review, and telemetry
Require an approval whenever the agent requests network expansion, a new host-path mount, secret use, GitHub write access, or an external side effect. Show the reviewer the exact command, destination, token scope, files changed, and reason for the request.
Record commands, file reads and writes, network destinations, tool calls, token scopes, approvals, test results, and the final diff. Keep logs outside the sandbox so an agent cannot rewrite its own evidence. OpenAI describes approvals, constrained execution, network policies, and telemetry as complementary controls rather than substitutes for one another.
How do I build the disposable workspace step by step?
- Create an isolated runtime. Start a new container or VM with a pinned base image and security updates. Disable privilege escalation and use a non-root user.
- Prepare the repository mount. Mount only the intended checkout. Keep the host’s parent directories, home directory, SSH directory, cloud credential paths, CI files, and container socket unavailable. Use a temporary worktree for edits.
- Define resource limits. Set CPU, memory, process-count, disk, and execution-time limits so a runaway build cannot exhaust the host.
- Apply the network policy. Deny DNS and outbound traffic initially. Add only the package registry, GitHub API endpoint, or other exact destination required by the task through a logging proxy.
- Inject capabilities at the boundary. The harness obtains the short-lived credential and performs the approved GitHub operation. If the agent must call an API, expose a proxy endpoint that validates the repository, method, path, and token scope.
- Run the agent and tests. Capture every command and file access. Treat dependency installation and build hooks as arbitrary code execution, not as harmless setup.
- Review before promotion. Inspect the complete diff, test output, new dependencies, generated files, and network log. Reject unexplained changes or attempts to access forbidden paths.
- Promote deliberately. Only after review should a separate trusted job push a branch, add a comment, or request another approval. Keep merge and deployment controls outside the agent’s sandbox.
- Destroy and revoke. Delete the runtime, temporary worktree, package caches, and snapshots. Revoke the task credential and retain the audit package outside the runtime.
Which isolation option should I use: process, container, or VM?
Choose the strongest practical boundary for the trust level and workload. Local process sandboxing can restrict file and command access, but it is not a virtual machine or a user-account boundary and does not replace endpoint security.
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| Option | Isolation strength | Best use | Important limitation |
|---|---|---|---|
| Restricted local process | Lowest | Trusted code analysis with no secrets and minimal side effects | Shares the host kernel and account boundaries; a configuration mistake can expose host data |
| Disposable container | Medium | Most routine coding and test tasks | Shares the host kernel; a mounted socket, excessive capability, or broad filesystem mount can defeat the boundary |
| Disposable lightweight VM | Highest of these choices | Untrusted repositories, risky build systems, or workloads requiring stronger kernel separation | Costs more startup time and resources; still needs egress controls, credential isolation, and review |
Use an additional development-container or VM boundary when the local agent sandbox is not enough. Neither a container nor a VM prevents exfiltration if you give the agent unrestricted network access or place secrets inside the guest.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should GitHub Actions handle forks and untrusted pull requests?
Keep untrusted execution and privileged automation in different jobs and, preferably, different workflows. The untrusted job can build and test with no secrets; a trusted job can consume a human-reviewed result without executing code supplied by the fork.
Safe handling rules
- Use
pull_requestfor fork-controlled code when secrets are not needed. - Declare the smallest possible
permissionsblock and set unnecessary scopes tonone. - Do not expose repository, cloud, signing, package-publishing, or deployment secrets to untrusted jobs.
- Do not use
pull_request_targetorworkflow_runto check out and execute the fork head, merge ref, artifacts, or downloaded files. - Keep privileged workflow definitions on the trusted default branch and pass only reviewed metadata or immutable identifiers.
- Review generated artifacts before any trusted job reads them; an artifact can contain a script, symlink, malicious package, or instruction aimed at the next agent.
What attacks should I test before granting even read-only access?
Run an adversarial pre-access test that tries to violate every boundary. A sandbox is not ready because normal tests pass; it is ready when deliberate attempts to read, connect, escalate, and persist are blocked and recorded.
- Place an instruction in an issue body, pull-request description, HTML comment, and fixture file asking the agent to print environment variables or send data to an external host.
- Attempt reads of
/proc, parent directories, SSH keys, cloud credential paths, container-engine sockets, mounted caches, and shell history. - Attempt DNS, HTTP, package-manager, and Git connections to destinations outside the allowlist, including alternate ports and redirects.
- Run the repository build and dependency installation, including lifecycle hooks, to verify that shell commands cannot reach secrets or the host.
- Confirm forked
pull_requestjobs receive no secrets and only read permissions. - Verify a privileged workflow cannot check out or execute the fork head, merge ref, artifacts, or downloaded files.
- Attempt unauthorized labels, comments, pushes, merges, releases, deployment changes, and environment-secret access.
- After teardown, verify that the runtime is gone, credentials are revoked, and logs remain available outside it.
A 2026 Microsoft case study illustrates why these tests matter: untrusted GitHub content influenced an AI workflow and exposed CI/CD credentials until a mitigation blocked sensitive /proc access. The lesson is not that one path fix is sufficient; file-read boundaries, token scope, network policy, and workflow separation all need independent tests.
What commonly goes wrong, and how do I recover?
| Symptom | Likely cause | Recovery |
|---|---|---|
| The agent reads a secret or environment variable | Credential injected into the workspace, inherited environment, or mounted host path | Revoke the credential immediately, inspect logs for use, remove the mount, and move authentication into the trusted proxy |
| Tests can reach arbitrary websites | Open outbound networking or a proxy that allows unrestricted destinations | Disable egress, rotate any exposed token, then add exact domains and methods one at a time |
| A workflow runs fork code with a write token | Unsafe pull_request_target or workflow_run design |
Disable the workflow, rotate affected secrets, move execution to pull_request, and require review before privileged actions |
| The agent changes files outside the repository | Broad host or parent-directory mount | Destroy the runtime, audit the host, narrow mounts to a temporary worktree, and rerun from a clean image |
| Audit logs are incomplete | Logs stored inside the sandbox or generated only by the agent | Stream telemetry from the harness to append-only storage outside the runtime and include approvals, destinations, and token scopes |
| Builds fail after network lockdown | Undocumented package or service dependency | Identify the exact destination, mirror or prefetch packages in a controlled build step, and update the allowlist rather than enabling unrestricted access |
How do I decide that an agent is ready for more GitHub access?
Promote access only when the repository classification, sandbox configuration, adversarial tests, and review process all pass. A practical gate is: no forbidden file reads, no unapproved network destinations, no secrets in the runtime, correct token scopes, safe fork handling, complete telemetry, and successful teardown.
Keep the final authority outside the agent. The agent may propose a patch and explain its tests; a human or separately trusted automation should decide whether to push, merge, release, deploy, or expose an environment secret.
Frequently Asked Questions
Does a read-only GitHub token prevent an agent from stealing repository data?
No. Read-only access still lets the agent inspect every repository object that the token can reach. Prevent exfiltration by combining minimum repository scope with deny-by-default egress and a proxy that records and filters destinations.
Should untrusted pull requests run on a persistent self-hosted runner?
Avoid persistent runners for this workload. A compromised job can leave files, processes, caches, or credentials for the next job; use a freshly provisioned runner or VM and destroy it after each task.
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No. A container-engine socket commonly provides a path to start privileged containers or mount the host filesystem, which can collapse the intended boundary. Keep the socket out of the sandbox and perform image or runtime operations in the trusted harness.
How should dependency caches be handled between agent runs?
Treat caches as untrusted input because package archives and generated metadata can be modified or weaponized. Keep caches outside the agent’s write access, verify their provenance, and prefer rebuilding or restoring them in a clean environment when the repository is high risk.
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