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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But the triumphance of V3 is in the addSourceBuffer hook which solves a subtle problem. In earlier versions, hooking SourceBuffer.prototype.appendBuffer at the prototype level had a vulnerability in that if fermaw’s player cached a direct reference to appendBuffer before the hook was installed (i.e., const myAppend = sourceBuffer.appendBuffer; myAppend.call(sb, data)), the hook would never fire. The player would bypass the prototype entirely and call the original native function through its cached reference.
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