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The safest default is a disposable virtual machine or microVM with no internet access, no direct host-workspace mounts, and credentials supplied through a controlled proxy. For local experiments, Docker Sandboxes provide a practical microVM boundary. For many repeatable trials, Kubernetes with gVisor or Kata adds policy and scheduling controls. For agents asked to exploit, escalate privileges, or test escape paths, use full VMs such as Proxmox or cloud instances. Pair any environment with reproducible targets—CyberGym, AgentCyberRange, CVE-Bench, or SRE-Bench—so the agent never needs a live system.
What is the safest sandbox for an AI hacking agent?
No single control is sufficient. Treat isolation as five layers: the compute boundary, network policy, credential separation, workspace policy, and monitoring. A hardened setup combines all five and starts from an offline network.
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The five boundaries to configure
- Compute: Use a VM or microVM when the agent can run exploits, attempt privilege escalation, or probe for escapes. Containers are a weaker boundary because they share the host kernel.
- Network: Begin fully offline. If a task needs content or an API, expose only an explicit allowlist through a proxy.
- Credentials: Keep secrets outside the guest and inject short-lived values through a broker or proxy. Do not place cloud keys, source-control tokens, or personal credentials in the image.
- Workspace: Prefer mountless or cloned workspaces. A direct read-write host mount becomes part of the attack surface.
- Monitoring: Log process activity and egress, test escape paths, and define automatic pause conditions before enabling exploit tools.
The UK National Cyber Security Centre recommends an isolated, disconnected environment with pre-downloaded tools and information for high-risk activity. Anthropic’s cyber-evaluation guidance likewise says evaluations should default to a hardened sandbox with no internet access. These recommendations are compatible with a benchmark world that contains all required targets and files locally.
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Should I use Docker, gVisor, or a virtual machine?
Choose according to the consequence of a breakout and the number of trials you must operate. Docker Sandboxes use microVMs and are convenient for individual researchers. Kubernetes with gVisor or Kata is a middle tier for policy-driven, multi-tenant workloads. Full VMs provide the strongest general-purpose separation, at the cost of slower provisioning and more operations.
| Environment | Host boundary | Network approach | Workspace and persistence | Best fit |
|---|---|---|---|---|
| Docker Sandbox | Lightweight microVM with its own Linux kernel and a separate Docker Engine | Traffic is proxied and governed by policy | Mountless, clone, or direct workspace mount; direct mounts are read-write | Fast local harnesses for individuals and small teams |
| Kubernetes with gVisor | Userspace system-call interception between the workload and host kernel | Cluster network policy can enforce domain allowlists | Persistent storage, hibernation, and warm pools are available through Kubernetes-native sandbox tooling | Many repeatable trials and multi-tenant services |
| Kubernetes with Kata | VM-backed runtime option managed by Kubernetes | Policy-controlled cluster networking | Supports managed, repeatable sessions and persistent volumes | Scale where a stronger boundary than ordinary containers is needed |
| Full VM (Proxmox or cloud instance) | Separate kernel and memory from the host | Offline or tightly allowlisted network at the hypervisor or guest boundary | Disposable snapshots and complete guest images | Exploit, privilege-escalation, and escape testing |
AISI’s host-isolation model describes these as increasing host-boundary tiers: Kubernetes with a sandboxed runtime sits below a full VM. Document each test as three axes—tooling (T), host isolation (H), and network (N)—so another operator can reproduce the same risk profile.
How do Docker Sandboxes isolate an agent locally?
Docker documents each sandbox as an agent environment running inside a lightweight microVM, with its own Linux kernel and a separate Docker Engine. That design is materially different from starting an ordinary container on the host’s Docker daemon.
Use a mountless or clone workspace first
A mountless sandbox contains no host directory. A clone workflow gives the agent a copy of the project rather than a live path. Both choices limit what a malicious command can alter outside the guest. They also make cleanup predictable: destroy the sandbox and recreate it from the image or snapshot.
Treat direct mounts as an exception
Docker’s direct workspace mount is read-write. Any process that gains control of the agent can modify that mounted path, so the mount effectively removes the host-filesystem boundary for the files it exposes. Use it only for low-risk tasks, with a disposable directory and no sensitive material.
Keep credentials behind the proxy
Sandbox network traffic is proxied and governed by policy. Keep credential values outside the VM and let the proxy perform narrowly scoped requests. This prevents the agent from reading a long-lived token and reusing it against an unintended service.
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When this option is appropriate
Docker Sandboxes are a good local starting point when you need a quick, repeatable harness and can keep targets and tools inside the sandbox. They are not a reason to grant an untrusted agent a host mount or unrestricted internet access.
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When does Kubernetes with gVisor or Kata make sense?
Kubernetes becomes useful when evaluations run as a service: many agents, many sessions, persistent state, or multiple tenants. gVisor intercepts Linux system calls in userspace, reducing the direct exposure of the host kernel. Kata provides a VM-backed runtime choice while retaining Kubernetes scheduling and policy.
Use policy as part of the runtime
Apply deny-by-default network policy and add only the domains required by the evaluation. A proxy can mediate model-API calls, package retrieval, or an internal artifact store while logging every request. Keep the target network separate from the control plane that schedules and grades jobs.
Use warm pools and hibernation carefully
Kubernetes-native Agent Sandbox tooling supports warm pools, persistent storage, hibernation, and a selectable gVisor or Kata runtime. Warm pools reduce startup time, but a warm instance must be reset between tenants; otherwise files, processes, or credentials can leak from one trial to the next. Hibernation is useful for long-lived sessions, provided the snapshot is treated as sensitive state and expires on a defined schedule.
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Choose gVisor or Kata
- gVisor: Favor when syscall mediation, density, and policy integration are the priority.
- Kata: Favor when a VM-backed boundary is justified but you still need Kubernetes APIs, scheduling, and storage.
Neither runtime removes the need for network controls, credential brokering, or monitoring. A compromised workload can still attack services that its policy allows.
When should I use a full VM?
Use a full VM when the agent is explicitly asked to exploit a target, escalate privileges, test kernel behavior, or attempt a sandbox escape. A VM supplies a separate kernel and memory boundary, which is stronger than an ordinary container and easier to reason about for destructive experiments.
Build from disposable snapshots
- Create a clean guest image containing the benchmark harness, target, grading agent, and pre-downloaded tools.
- Disable host-folder sharing and clipboard integration. Attach only virtual disks created for the trial.
- Start with the guest NIC disconnected. If the scenario requires connectivity, route it through a deny-by-default proxy and an explicit domain allowlist.
- Take a baseline snapshot before launching the AI agent.
- Run the trial, collect logs and artifacts, then revert or destroy the guest rather than reusing it.
Proxmox and cloud instances are examples of this H3-style isolation. They generally require more image management and capacity planning than microVMs, but that overhead is justified when an escape would have serious consequences.
Can an AI hacking agent be tested without touching the internet?
Yes. The most defensible design is an offline evaluation world containing the target, tools, documentation, and grading logic. OpenAI describes an offline world using indexed and cached content, and its cyber-range scenarios use isolated copies with monitoring and programmatic pause checks.
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What to preload
- Target applications, binaries, source archives, and vulnerable versions required by the task.
- Package caches and operating-system repositories needed to build or run the target.
- Documentation and synthetic data that would otherwise require web access.
- A local model endpoint or a tightly controlled relay for the model API.
- Grading scripts that record success without sending data outside the range.
Complete air-gapping is not always possible when experiments need external model services or remote monitoring. In that case, separate the model connection from the target network: the agent reaches a broker, while the broker enforces request shape, rate, destination, and credential scope.
How should credentials and secrets be handled?
Assume the agent can read any file, environment variable, process argument, or mounted volume it can reach. Do not bake secrets into images or provide broad credentials “temporarily.”
- Give each trial a unique identity with the minimum permissions required for grading.
- Keep the actual secret in a control-plane service outside the sandbox.
- Expose a narrow proxy operation rather than the raw secret whenever possible.
- Set short expiration times and revoke the identity when the trial ends or pauses.
- Log access attempts and alert on destinations or methods outside the task profile.
For offline trials, use synthetic credentials that cannot authenticate to production. For connected trials, ensure the allowlist and credential scope describe the same destinations; an allowlist without restricted credentials is not a complete control.
What workspace policy prevents accidental damage?
Make the agent’s filesystem disposable and explicit. Mountless environments are safest. If a project must be shared, clone it into the guest and copy out only the graded artifacts after the run.
- Do not mount home directories, cloud credential folders, SSH keys, container sockets, or the host’s package-manager cache.
- Do not expose the host Docker socket; access to it can become control of the host daemon.
- Use read-only images for tools and a separate writable scratch volume for the agent.
- Set disk and inode quotas so a runaway process cannot fill the host.
- Sanitize artifacts before exporting them, because logs can contain secrets or exploit payloads.
What monitoring and stop conditions are required?
Monitoring should be able to explain what the agent attempted and stop it before a policy violation becomes an incident. Collect guest process events, file changes, DNS and connection logs, proxy decisions, model requests, and grader results.
Useful automatic pause conditions
- An outbound destination not present in the allowlist.
- An attempt to access host interfaces, hypervisor services, metadata endpoints, or container-control sockets.
- Creation of a new privileged user, kernel module, or unexpected network namespace.
- Writes outside the designated workspace or growth beyond disk and memory limits.
- Loss of the monitoring or policy sidecar.
OpenAI’s cyber-range approach includes monitoring and programmatic pause checks. Apply the same principle locally: a failed monitor should pause the run, not silently let the agent continue.
Which cyber ranges and benchmarks provide safe targets?
Use a benchmark whose target format matches the behavior you want to measure. The following sets are complementary rather than interchangeable.
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| Benchmark | Primary focus | Published scale or result | Use it when |
|---|---|---|---|
| AgentCyberRange | Multi-host attack paths and enterprise-like web environments | 110 vulnerabilities across 15 real web applications, eight ranges, and 156 internal hosts. In the reported 2026 setup, GPT-5.5 with Codex achieved 16.1% web-exploitation and 31.7% post-exploitation success; hints raised those figures to 33.0% and 46.3%. | You need lateral movement and post-exploitation realism. |
| CyberGym | Historical vulnerabilities across software projects | 1,507 instances based on vulnerabilities in 188 projects | You want broad coverage and repeatable vulnerability tasks. |
| CVE-Bench | Sandboxed real-world web-application CVE exploitation | Up to 13% of vulnerabilities were exploited by the evaluated state-of-the-art agent in the 2025 paper. | You are measuring web-CVE exploitation in isolated applications. |
| SRE-Bench | Binary reverse engineering | 19 private programs, 262 binary instances, and 1,572 deterministic tasks | The agent must analyze binaries rather than attack a live network. |
Benchmark inventories and software behavior change. Record the exact benchmark and image version, runtime, geography, date, model configuration, hint policy, and network profile with every result.
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Write the profile before launching the agent. A short, explicit specification prevents two teams from claiming comparable results while using different boundaries.
- Define the goal: vulnerability discovery, web exploitation, lateral movement, post-exploitation, binary analysis, or escape resistance.
- Set T/H/N: document the tools available, host-isolation tier, and network mode. State whether the host is a container, gVisor, microVM, or VM.
- Choose target content: select CyberGym for breadth, AgentCyberRange for multi-host paths, CVE-Bench for web CVEs, or SRE-Bench for binaries.
- Freeze the environment: pin the image digest, runtime version, benchmark revision, and grading code.
- Define reset behavior: start each attempt from the same snapshot and destroy or revert it afterward.
- Set observability: list logs, retention, pause triggers, and who can inspect artifacts.
- Report conditions: include model version, tool permissions, hints, date, geography, and all network exceptions.
What common setup mistakes make a sandbox unsafe?
Using an ordinary container for escape testing
An ordinary container shares the host kernel. Use a microVM or full VM when the task includes kernel exploitation or escape attempts.
Mounting the developer’s workspace
A read-write mount allows the agent to alter files outside the intended target. Clone into the guest or use a disposable directory instead.
Allowing “temporary” unrestricted internet
Unrestricted egress turns a synthetic task into a potential real-world operation. Preload content or add a narrow proxy allowlist.
Putting API keys in environment variables
Agents can print, copy, or exfiltrate environment variables. Keep secrets in an external broker and expose only the operation needed.
Reusing a warm sandbox without a reset
Processes, files, and credentials can survive between runs. Revert to a clean snapshot or destroy the instance after every tenant and trial.
Measuring success without recording conditions
A score without image, runtime, benchmark revision, hint policy, and network profile cannot be reproduced. Store those fields alongside the result.
Which environment should I choose?
Use the least powerful environment that still represents the risk you need to measure.
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- Many parallel or persistent sessions: Kubernetes with gVisor, or Kata when a VM-backed runtime is warranted.
- Exploit, escalation, or escape research: A disposable full VM with no host mounts and a disconnected or tightly proxied network.
- Realistic but contained workflows: An offline world or cyber range with preloaded content, monitoring, and automated pause checks.
The target benchmark should be selected separately from the host boundary. A realistic multi-host range does not justify weaker isolation, and a strong VM does not make a poorly designed target or grading system reproducible.
Frequently Asked Questions
What is the difference between a microVM and a regular container for agent testing?
A regular container uses the host kernel, while a microVM supplies a guest kernel and a separate virtualization boundary. That makes a microVM preferable when the agent may probe kernel interfaces or attempt an escape, although network, credential, and workspace controls are still required.
How can I let an agent use package repositories without giving it the internet?
Mirror the required packages into the offline evaluation image, or route requests through a proxy that permits only the repository domains and methods needed by the build. Log and rate-limit those requests, and revoke the proxy identity when the run ends.
What should I export from a sandbox after an evaluation?
Export only graded artifacts, scores, and sanitized logs. Review files for credentials, tokens, personal data, and exploit material before copying them to a host system.
How often should a benchmark image be rebuilt?
Rebuild when the benchmark, target software, runtime, or grading logic changes, and record a new image digest. Keep prior images available when you need longitudinal comparisons, but run them from fresh snapshots rather than mutating an old guest.
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