Thomas Piacentini
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Project

Sandbox Policy Planner

Original text · English480 min

A sandbox evidence planner mapping requested capabilities to observed runtime probes.

Write the threat requirement as booleans before selecting a runtime. Here network=true means network denial is required, and host_kernel=true means a separate kernel is required. Neither name grants access.

Start with a learner workspace

Docker for AI, Security and secrets audit. Language foundation: Rust ownership and Result.

Install Rust (rustc) and Python 3.10+. The core uses the Rust standard library; Python adapters compile into private temporary directories.

bash
python3 scripts/project_test.py sandbox-ladder --init learning-artifacts/sandbox-ladder
python3 scripts/project_test.py sandbox-ladder --stage 1 --path learning-artifacts/sandbox-ladder

Build route

  1. Parse the capability request
  2. Define modeled control profiles
  3. Select the least costly sufficient profile
  4. Report residual assumptions

Run with your own inputs

After completing the stages, these commands run your workspace code on the original Orchard examples. Replace the sample paths with your own files.

bash
python3 learning-artifacts/sandbox-ladder/cli.py untrusted=true,secrets=true,network=true,host_kernel=false --budget 3

To inspect the complete reference first, replace learning-artifacts/sandbox-ladder with projects/sandbox-ladder/solution in the same command. JSON results use schema_version: 1; paths and argument examples are explicit so another tool can consume them.

Integration boundary

Default output is a policy simulation. --docker-image previews an invocation; --execute requires an already available local image and Docker. Only root-write and default-route probes are tested. The adapter never implements or verifies a microVM boundary.

bash
python3 scripts/project_test.py sandbox-ladder --all --solution --strict
python3 scripts/project_test.py sandbox-ladder --all --path learning-artifacts/sandbox-ladder --strict

The first command checks the reference. The second checks your implementation. The published examples and tests are regression evidence, not a production certification or an unseen benchmark.

Primary references

Code and artifacts 33
What you take away

Saved in your notebook on this device.

Sources and limits

Reading checkpoints are self-reported. Test results are recorded separately and apply to the executed cases.

AI Engineering from Scratch · 3f9715c · MIT

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