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For reinforcement learning training pipelines where AI-generated code is evaluated in sandboxes across potentially untrusted workers, the threat model is both the code and the worker. You need isolation in both directions, which pushes toward microVMs or gVisor with defense-in-depth layering.
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The answer is essentially hardware-level dependency injection. Before calling LD_DESCRIPTOR, the caller saves its desired test constant into a hardware latch using a micro-op called PTSAV (Protection Save). Within LD_DESCRIPTOR, another micro-op called PTOVRR (Protection Override) retrieves and fires the saved test.