'Local' Solves Where Your Data Goes. It Doesn't Solve What Your Agent Does

Short summary

Local LLM deployment solves data sovereignty but does not mitigate prompt injection, provenance failures, or privilege escalation—these are architectural, not infrastructure problems. Prompt injection success rates remain 50–85% regardless of where the model runs, and local filesystems introduce new indirect injection vectors inside the trusted perimeter. Local agents are safe only for bounded-scope tasks with trusted inputs and reversible actions; the dividing line is what the agent can touch, not where it runs.

• •Local deployment fixes data location only; prompt injection and agent behavior risks persist unchanged
• •Indirect injection via local files/logs is arguably harder to detect on-prem than in cloud
• •EU AI Act high-risk provisions (effective Aug 2026) make data sovereignty valuable but don't address agent safety

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