There is a lot of energy right now around sandboxing untrusted code. AI agents generating and executing code, multi-tenant platforms running customer scripts, RL training pipelines evaluating model outputs—basically, you have code you did not write, and you need to run it without letting it compromise the host, other tenants, or itself in unexpected ways.
Ранее мужчины и женщины старше 40 лет рассказали молодым о самых важных вещах в жизни. Многие признались, что по-настоящему ценным оказалось общение с семьей и близкими людьми.
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The common pattern across all of these seems to be filesystem and network ACLs enforced by the OS, not a separate kernel or hardware boundary. A determined attacker who already has code execution on your machine could potentially bypass Seatbelt or Landlock restrictions through privilege escalation. But that is not the threat model. The threat is an AI agent that is mostly helpful but occasionally careless or confused, and you want guardrails that catch the common failure modes - reading credentials it should not see, making network calls it should not make, writing to paths outside the project.
Supervised runtime behavior