Free Sandbox Tool Sandbox Runtime: Blocks AI Agents from Reading SSH Keys

AI coding agents boost productivity but also introduce new security risks—they might read your SSH keys or access unauthorized websites. An open-source tool called Sandbox Runtime aims to solve this problem.

What Is Sandbox Runtime?

According to a YouTube video, Sandbox Runtime is a free sandbox tool from Anthropic. It is the underlying sandbox technology that powers Claude Code. The project is open source under the Apache 2.0 license and has over 6,000 stars on GitHub.

Its core function is to limit the scope of what AI agents can do: agents cannot access websites you haven't explicitly allowed, nor can they read private files (such as SSH keys).

Sandbox is a security mechanism that creates an isolated runtime environment for programs, restricting the system resources they can access (such as files, networks, processes, etc.). In the context of AI agents, a sandbox prevents agents from performing operations beyond their authorization, such as reading sensitive files or connecting to malicious servers. Common implementations include OS-level isolation (such as seccomp, namespaces), containerization (such as Docker), or lightweight runtime interception. Sandbox Runtime belongs to the latter category—it does not rely on full virtualization but instead intercepts and enforces policies at the system call level, providing fine-grained permission management while maintaining performance.

How to Use It?

The installation process is very simple—just one npm command. After installation, simply prefix any command with srt to enable the sandbox.

For example, instead of running the claude command, run srt claude, and that process will be constrained by the sandbox.

No Docker, no containers needed. It runs directly on your system and currently supports Mac and Linux.

npm (Node Package Manager) is a package management tool for JavaScript, widely used to install and manage dependencies for Node.js projects. You can globally install command-line tools via npm install -g. The srt prefix is a transparent proxy approach: when you execute srt <command>, srt launches a sandbox-constrained child process to run the original command, and all filesystem and network access goes through policy checks. This approach requires no modifications to your existing toolchain, lowering the barrier to adoption.

Why Does It Matter?

AI agents typically need filesystem and network access to complete tasks, but this also means they might inadvertently or maliciously read sensitive data. SSH keys are among the most critical credentials for developers—if leaked, they can lead to server compromise.

Sandbox Runtime enforces isolation so that agents can only access explicitly permitted resources.

SSH keys are cryptographic credentials used for passwordless login to remote servers, typically stored in the ~/.ssh/ directory (such as id_rsa). Once leaked, an attacker can directly log into the associated servers, causing data breaches or system damage. AI coding agents assisting with development may scan the filesystem as part of their tasks, and without isolation, they could easily read or exfiltrate keys by accident. Sandboxes effectively block such risks by restricting file read paths and network egress.

Use Cases

According to the video, the tool is well-suited for NCP servers and coding agents. For teams using AI agents for development, it provides a simple and effective security layer.

NCP servers typically refer to NVIDIA Cloud Playground or similar high-performance computing cloud environments, commonly used for AI model training and inference. When running coding agents on such servers, the sandbox prevents accidental operations from affecting the host machine or other tenants. Coding agents such as Claude Code and GitHub Copilot need access to code repositories and network resources, and the sandbox ensures their behavioral boundaries remain controllable without sacrificing functionality—especially suitable for multi-user or production environments.

A Few Thoughts

Being open source and free lowers the adoption barrier, but the effectiveness of the sandbox depends on how strict the configuration is. Users need to clearly define which resources and paths are allowed; otherwise, they may fall into a false sense of security. Additionally, it currently only supports Mac and Linux—Windows users will have to wait.

Would you add a sandbox to your AI agent?