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Phase 02: Safety & Sandboxing - 4 plans in 3 waves - Security assessment, sandbox execution, audit logging, integration - Wave 1 parallel: assessment (02-01) + sandbox (02-02) - Wave 2: audit logging (02-03) - Wave 3: integration (02-04) - Ready for execution
4.8 KiB
4.8 KiB
phase, plan, type, wave, depends_on, files_modified, autonomous, must_haves
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| 02-safety-sandboxing | 02 | execute | 1 |
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Purpose: Isolate generated code execution using Docker containers with strict resource controls, read-only filesystems, and network restrictions as defined in CONTEXT.md. Output: Working sandbox executor that can run Python code securely with real-time resource monitoring.
<execution_context>
@/.opencode/get-shit-done/workflows/execute-plan.md
@/.opencode/get-shit-done/templates/summary.md
</execution_context>
Research references
@.planning/phases/02-safety-sandboxing/02-RESEARCH.md
Task 1: Create Docker sandbox manager src/sandbox/__init__.py, src/sandbox/container_manager.py Create ContainerManager class using Docker Python SDK. Implement create_container(image, runtime_configs) method with security hardening: --cap-drop=ALL, --no-new-privileges, non-root user, read-only filesystem where possible. Support network_mode='none' for no network access and network whitelist for read-only internet access. Include cleanup methods for container isolation. python -c "from src.sandbox.container_manager import ContainerManager; print('ContainerManager imported successfully')" ContainerManager creates secure containers with proper isolation, resource limits, and cleanup Task 2: Implement sandbox execution interface src/sandbox/executor.py, config/sandbox.yaml Create SandboxExecutor class that uses ContainerManager to run Python code. Execute code in isolated containers with configurable limits from config/sandbox.yaml (2 CPU cores, 1GB RAM, 2 minute timeout for trusted code). Implement real-time resource monitoring using docker.stats(). Handle execution timeouts, resource violations, and return results with security metadata. python -c "from src.sandbox.executor import SandboxExecutor; print('SandboxExecutor imported successfully')" SandboxExecutor can execute Python code securely with resource limits and monitoring Task 3: Configure sandbox policies config/sandbox.yaml Create config/sandbox.yaml with sandbox policies matching CONTEXT.md decisions: resource quotas (cpu_count: 2, mem_limit: "1g", timeout: 120), security settings (security_opt: ["no-new-privileges"], cap_drop: ["ALL"], read_only: true), and network policies (network_mode: "none" with whitelist for dependency access). Include dynamic allocation rules based on trust level. python -c "import yaml; print('Config loads:', yaml.safe_load(open('config/sandbox.yaml'))')" Configuration defines sandbox security policies, resource limits, and network restrictions - ContainerManager creates Docker containers with proper security hardening - SandboxExecutor can execute Python code in isolated containers - Resource limits are enforced (CPU, memory, timeout, PIDs) - Network access is properly restricted - Container cleanup happens after execution - Real-time resource monitoring works<success_criteria> Docker sandbox execution environment ready with configurable resource limits, security hardening, and real-time monitoring for safe code execution. </success_criteria>
After completion, create `.planning/phases/02-safety-sandboxing/02-02-SUMMARY.md`