Python Sandboxing Debate: Is Pyodide in Deno the Best Approach?

The release of Pydantic's Model Context Protocol (MCP) for running Python code in a sandbox has sparked a lively debate among developers about the best approaches to Python sandboxing. The solution, which uses Pyodide running in Deno to execute Python code in isolation, has drawn both praise and criticism from the developer community.

Security Trade-offs in Python Sandboxing

The MCP Run Python implementation executes Python code within Pyodide, a Python distribution for the browser that runs inside Deno's JavaScript runtime. While the approach provides isolation from the host operating system, community members have raised concerns about the security assumptions. Some developers point out that this method relies on the security of multiple layers - Deno's WASM VM and Pyodide - both of which could potentially contain exploits or bugs. The debate highlights the fundamental challenge of running untrusted Python code securely, as CPython (the standard Python implementation) wasn't designed with sandboxing as a primary feature.

I trust a WASM sandbox a whole lot more than I trust a Docker container sandbox. WASM engines run in almost every browser on earth, billions of times a day. Security problems in those get spotted very quickly.

Python Sandboxing Approaches Mentioned in Discussion

• Pyodide in Deno (MCP Run Python): Uses WASM-based isolation, good security but performance overhead
• Wasmtime: Running Python in WASM container directly inside Python
• Firecracker microVMs: Better security isolation but slower startup time
• Linux kernel features: Landlock, cgroups for sandboxing
• gVisor: Container sandbox technology
• Custom sandboxing: Using eval/exec with custom importers (mentioned by Temporal)
• Seccomp-based jails: For limited use cases with restricted syscalls

Performance Considerations and Alternatives

Performance metrics shared in the discussion reveal significant overhead when running Python code in a WASM-based sandbox. Benchmarks showed that a simple hello world program in the sandbox was approximately 12 times slower than standard Python execution, and nearly 370 times slower than an optimized C implementation of the same program. This performance gap has led developers to explore alternative approaches, including using Linux kernel features like Landlock and cgroups, firecracker microVMs, and other WASM-based solutions like wasmtime.

Performance Comparison (Hello World Program)

Ecosystem Support and Practical Applications

Despite performance concerns, the Pyodide approach offers surprising flexibility. Community members noted that complex dependencies like scikit-learn work in this environment, enabling simple machine learning experiences in the browser. This capability makes the solution attractive for certain use cases, particularly when security is a higher priority than raw performance. The approach also mirrors techniques used by major AI platforms - for example, ChatGPT uses Pyodide for browser-based code execution and Jupyter in Kubernetes containers for its Code Interpreter feature.

The Broader Context of AI Agent Frameworks

The MCP Run Python implementation is part of a growing ecosystem of AI agent frameworks that enable language models to execute code safely. Some developers expressed concern about the proliferation of these frameworks, drawing parallels to the fragmentation seen in the JavaScript ecosystem. Alternative approaches mentioned include Dylibso's eval-py, firecracker VMs for more flexible but slower execution, and custom sandboxing solutions built with tools like seccomp.

The search for the perfect Python sandboxing solution continues, with the ideal approach depending heavily on specific use cases, security requirements, and performance needs. As AI systems increasingly need to execute code safely, this area is likely to see continued innovation and refinement.

Reference: MCP Run Python