The sandbox profiles of Gemini CLI and the isolation methods of Codex CLI differ fundamentally in their architecture, security approach, implementation details, and user experience. These differences reflect their distinct design philosophies, technology stacks, and target use cases. Below is a comprehensive analysis of these differences, broken down into key areas.
Core Technology and Architecture
Gemini CLI is primarily a TypeScript/Node.js-based system. Its CLI and backend components are written in TypeScript, which emphasizes accessibility and integration within the JavaScript ecosystem. This means Gemini is easier for web and Node.js developers to understand and extend but relies more on environmental isolation tools for security.
Codex CLI, on the other hand, is a hybrid system with a significant Rust core. The core logic, security policies, and execution components are implemented in Rust, while the CLI frontend remains in TypeScript/React. This hybrid architecture leverages Rust's performance, memory safety, and systems-level control for a more sophisticated and tightly controlled sandbox environment. Codex even includes a custom policy engine formulated in Rust, which supports extensive security customization beyond basic containerization.
Sandboxing and Isolation Mechanisms
Gemini CLI uses several sandboxing methods tailored to different environments:
- Containerization: The most secure option involves running Gemini CLI inside Docker or Podman containers. Containers provide a full OS-level isolation including separate filesystems and network stacks, effectively isolating the AI processes from the host machine.
- macOS Seatbelt (sandbox-exec): For macOS users preferring a lighter-weight option, Gemini CLI can utilize the system's native sandboxing tool, providing restricted capabilities primarily focused on file system and network access.
Gemini CLI's sandbox profiles control what the AI can access or modify, typically restricting write access outside the project directory while optionally allowing network access. It uses predefined Seatbelt profiles such as "permissive-open", "restrictive-closed", etc., configurable via environment variables or CLI flags. These profiles enable a granular yet rule-based security setup that is relatively straightforward but mostly "all or nothing" in practice.
Codex CLI approaches isolation with a more sophisticated, programmable security model:
- It integrates platform-level sandboxing like macOS Seatbelt and Linux Landlock for lightweight OS-level isolation.
- More importantly, it employs a custom execution policy engine written in Rust. This engine uses Starlark (a Python-like scripting language) to define detailed execution policies, specifying which shell commands and arguments are allowed or forbidden.
- This policy-driven approach permits finely-grained control over what operations are safe, enabling Codex to safely execute complex workflows with minimal user intervention.
This goes well beyond Gemini's more traditional container or OS sandboxing by enabling application-aware security policies that adapt to specific contexts, balancing operational flexibility with enhanced protection against malicious or erroneous commands.
Security Models and Use Cases
Gemini CLI's sandbox is designed to prevent accidental or malicious damage from AI-generated code by isolating it within containers or lightweight sandboxes. It prioritizes user consent and layered defense, requiring explicit approval for risky operations. This isolation is effective at protecting the host system's files and settings, confining AI operations to the project workspace.
Codex CLI's security model, built around its Rust policy engine, is more proactive and integrated. It allows Codex to autonomously manage tasks within highly constrained environments by predefining safe operations at a granular command and argument level. This supports more seamless automation and reduces the need for manual user interventions during complex workflows while maintaining strong safeguards.
In practice, Codex's method is more suitable for environments where high operational autonomy and trust in AI decision-making are required, such as large parallelized or long-running coding tasks running unattended. Gemini's sandbox focuses on accessibility, clear boundaries, and user-mediated security, ideal for scenarios where explicit user control is preferred.
User Experience and Configuration
Gemini CLI users enable sandboxing via a simple `--sandbox` flag or corresponding environment variables/settings (e.g., enabling Docker as the sandbox backend). The sandbox can be configured with built-in profiles tailored for common security postures, from permissive to highly restrictive modes. Users can inject Docker or Podman flags, override UID/GID mapping, and choose between lightweight or container-based sandboxes based on their platform and preferences.
Codex CLI's sandboxing and execution policies are embedded more deeply within its architecture. Users write or modify policy files in Starlark, defining which commands, tools, or arguments the AI can utilize. This policy scripting gives advanced users and organizations fine-tuned control, but also adds complexity compared to Gemini's more plug-and-play sandbox profiles.
Summary of Differences
- Technology Stack: Gemini is TypeScript/Node.js-centric; Codex uses hybrid TypeScript and Rust.
- Sandboxing Methods: Gemini employs containerization or macOS Seatbelt with configurable predefined profiles; Codex uses Rust-based custom policy engine plus light OS sandboxes.
- Security Model: Gemini relies on environment-level isolation with user consent for risky actions; Codex uses programmable, application-aware security policies allowing autonomous and fine-grained control.
- Isolation Granularity: Gemini's sandbox is mostly all-or-nothing containment; Codex enables command-level policy decisions.
- User Control vs Autonomy: Gemini's model prioritizes explicit user control; Codex supports higher operational independence through nuanced security policies.
- User Experience: Gemini offers straightforward sandbox toggles with set profiles; Codex requires editing policy scripts, better suited for advanced users or organizations.
In essence, Gemini CLI's sandbox profiles provide practical, accessible containment primarily via container or OS sandbox environments designed for secure but user-controlled AI code execution. Codex CLI's isolation is a deeper, more advanced integration of sandboxing and policy enforcement that enables fine-tuned, autonomous, and safer execution of AI-generated code and workflows. This makes Codex's approach generally more powerful and flexible but potentially more complex to configure compared to Gemini's straightforward sandbox profiles.
This detailed comparative overview captures the essential differences in design, security philosophy, and user experience between Gemini CLI's sandbox profiles and Codex CLI's isolation methods. Each approach has distinct advantages aligned with different user needs and operational contexts.