Sandboxed Code Execution: Run AI-Generated Code Without Risk

The problem with running AI-generated code

AI-generated code is getting good. Models like Claude and GPT-4 can write Python scripts, Node.js services, Bash automations, and data pipelines that actually work. The code itself is not the problem.

The problem is where it runs.

If you are using OpenClaw or a similar local AI agent, that code runs directly on your machine. Full system access. Full filesystem access. Full network access. One malicious script — or one hallucinated rm -rf — and your development environment is gone.

Security researchers have documented significant vulnerabilities in local AI agent frameworks — from malware in skill marketplaces to plaintext credential storage and remote code execution exploits. Multiple security organizations have published warnings.

You wanted an AI agent that writes and runs code. You got a security risk with unpredictable API costs attached.

What sandboxed execution actually means

LikeClaw runs your code in E2B containers — isolated cloud sandboxes that are created for each task and destroyed when the task completes.

Here is what that means in practice:

• No system access. The container cannot read your filesystem, access your network, or touch your credentials. It runs in total isolation from your machine and from every other user’s environment.
• Created per task. Every execution gets a fresh container. No leftover state from previous runs. No contamination between tasks. No accumulated cruft.
• Destroyed after completion. When the task finishes, the container is wiped. Any code that tried to persist malware, phone home, or establish a backdoor is gone. Permanently.
• Encrypted credentials. Your API keys are encrypted at rest and injected into the container at runtime. They are never written to disk in plaintext. They are never accessible to other users or skills.

This is the same sandboxing approach used by Cloudflare Workers, AWS Lambda, and Vercel’s serverless functions. It is a proven architecture for running untrusted code safely. We applied it to AI agents because someone had to.

What you can do with sandboxed code execution

The sandbox is not a toy. It is a full execution environment. Here is what you can build:

Run scripts in any supported language. Python data processing, Node.js API integrations, Bash automation scripts. Install packages on the fly. The sandbox comes with common libraries pre-installed, and you can add whatever you need.

Process and transform data. Parse CSVs, clean JSON, aggregate API responses, generate reports. Upload your data, describe what you want, and the AI writes and runs the code in the sandbox. Download the results. Your raw data never leaves the isolated container.

Automate builds and tests. Generate unit tests from your codebase. Run test suites in a clean environment. Check dependencies for known vulnerabilities. Validate build scripts before pushing them to your CI pipeline. Every run starts fresh — no flaky tests from accumulated state.

Generate files and artifacts. PDFs, charts, spreadsheets, images, configuration files. The AI writes the code, the sandbox runs it, and you download the output. No local toolchain required.

Persistent workspaces: pick up where you left off

Sandboxed does not mean ephemeral. Your workspace persists between sessions. Files you generate, datasets you upload, outputs you create — they are all saved to your encrypted workspace and available the next time you start a task.

The sandbox itself is destroyed after every execution. But your workspace mounts into each new sandbox, giving you continuity without compromising isolation. Think of it as a secure locker that connects to a fresh, clean workbench every time you sit down.

Pick the right model for the job

Not every code task needs the same AI model. LikeClaw gives you access to 100+ models through a single interface:

• Claude — best for complex logic, nuanced code review, and multi-step reasoning
• GPT-4 — strong general-purpose coding and natural language understanding
• Gemini — solid for tasks that benefit from Google’s training data and multimodal inputs
• DeepSeek — cost-effective for straightforward tasks where you want to conserve credits

Switch models between tasks. Or let LikeClaw pick the best model automatically based on the task type. On the Power plan, bring your own API keys for zero markup.

One subscription. Every model. No juggling four dashboards and four invoices.

How this fits your workflow

Sandboxed code execution is not a replacement for your IDE or your CI/CD pipeline. It is the layer that sits between “the AI wrote some code” and “I trust this enough to run on my machine.”

Use it to prototype scripts before committing them. Use it to process sensitive data without exposing it to your local environment. Use it to run untrusted code from AI models that — as good as they are — still hallucinate sometimes.

If you are evaluating local AI agents and the security model concerns you, read why sandboxed AI agents are the future. If you want to see sandboxed execution applied to data workflows specifically, check out the data analysis use case.

Your code should run. Your machine should stay safe. These two things are not in conflict. They just need a sandbox.