"Work perfectly and leak data constantly" is the dangerous combination, because the leaking version passes every functional test, the agent answers correctly, and the data exposure is invisible until an audit or a breach. My read on the fatal flaw, and tell me if this matches yours: standard API integrations hand the agent broad credentials and over-scoped access because that's what makes the happy path easy, so the agent technically can reach far more than the task needs, and either an injection or just over-eager retrieval turns that latent access into an actual leak. The correct design moves the boundary off the credential and onto the request: scope what the agent can read per task, enforce access at retrieval (so it can't even fetch what it shouldn't surface), and never rely on the model to remember not to expose something it was allowed to load. The data the agent never receives can't leak. That's the load-bearing wall, access control at the data boundary, not output filtering after the model already saw it. That enforce-at-the-boundary-not-the-output stance is core to how I think about agent security in Moonshift. Is the fatal flaw you're pointing at the over-scoped credential, or context bleed where one request's data persists into another?
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"Work perfectly and leak data constantly" is the dangerous combination, because the leaking version passes every functional test, the agent answers correctly, and the data exposure is invisible until an audit or a breach. My read on the fatal flaw, and tell me if this matches yours: standard API integrations hand the agent broad credentials and over-scoped access because that's what makes the happy path easy, so the agent technically can reach far more than the task needs, and either an injection or just over-eager retrieval turns that latent access into an actual leak. The correct design moves the boundary off the credential and onto the request: scope what the agent can read per task, enforce access at retrieval (so it can't even fetch what it shouldn't surface), and never rely on the model to remember not to expose something it was allowed to load. The data the agent never receives can't leak. That's the load-bearing wall, access control at the data boundary, not output filtering after the model already saw it. That enforce-at-the-boundary-not-the-output stance is core to how I think about agent security in Moonshift. Is the fatal flaw you're pointing at the over-scoped credential, or context bleed where one request's data persists into another?