EXPLAINER

Code Execution and Sandboxing for AI Agents

techniquefreshness: evolvingsources not yet verified

Agents that write and run code need isolation: sandboxes limit filesystem, network, resources and credentials so a mistake or injected instruction cannot harm the host.

Short answer: Run model-generated code in a disposable, restricted environment: no secrets, limited network, CPU/memory/time caps, read-only mounts. Containers help but are not equal to microVM/gVisor-style stronger isolation. Never execute generated code on a machine holding credentials.

What is it?

Letting an AI run code is powerful and risky — a bug or a trick could delete files or leak secrets. A sandbox is a locked room: the code runs there, with only what it needs, and the room is thrown away afterwards.

Also known as: code interpreter, sandbox for LLM code, run AI generated code safely, agent sandbox, gVisor, Firecracker microVM, code execution tool, secure code execution.

Technical definition

Isolation options: containers (namespaces/cgroups; shared kernel), user-space kernels such as gVisor, microVMs such as Firecracker, WebAssembly runtimes, and hosted code-interpreter services. Controls: non-root users, seccomp/AppArmor profiles, resource limits, ephemeral filesystems, egress firewalls or allow-lists, no mounted credentials, timeouts, output size caps and auditing. Threats include escape, resource exhaustion, data exfiltration and malicious dependencies.

Entity type: technique · Freshness class: evolving.

When should I use it?

Required whenever agents execute code or shell commands.

Implementation patterns

  • Ephemeral container per run with CPU/memory/time limits.
  • No network by default; allow-list when needed.
  • Pass inputs as files; return outputs through a narrow channel.
  • Static scan and package allow-lists.

Technologies and frameworks

Commonly associated: Docker, gVisor, Firecracker, WebAssembly runtimes, hosted code-interpreter services. Names are examples of what exists, not recommendations, and projects change quickly.

Practical guide

  1. Define what code may do.
  2. Choose an isolation level matching the threat model.
  3. Strip secrets; restrict network.
  4. Set limits and timeouts.
  5. Log and review executions.

Common failure modes

  • Mounting host directories or credentials.
  • Unrestricted network egress.
  • Reusing state between users.
  • Resource exhaustion.
  • Assuming containers are a hard security boundary.

Troubleshooting

  • Code needs packages — Pre-build an image with approved libraries instead of allowing arbitrary installs.
  • Concern about escape — Use stronger isolation (microVM/gVisor) and run on dedicated infrastructure.

Contrasted with

Questions this page answers

  • How do I safely run code generated by an LLM?
  • Is Docker enough to sandbox AI-generated code?
  • What is a code interpreter tool?
  • What is gVisor?

Typical goals: run generated code safely; choose isolation technology.

Sources and verification

Status: not independently verified. This entry was authored on 2026-10-06 from general technical knowledge, without live checking of the sources below. Treat every source as a lead to confirm (identifier, title, authorship, current version) before publication or citation.

Freshness class: evolving — Established techniques whose best practice shifts as research and tooling move. Re-review every 6 months.

Canonical sources (unverified leads):

  • OWASP guidance on LLM application security — genai.owasp.org
  • Documentation for the chosen sandbox technology (gVisor, Firecracker, container runtime) — locator to be identified