Task-Conditioned Least-Privilege Learning for Executable Terminal and MCP Agents
Tool-using large language-model agents can complete a task while exercising authority that the user did not grant or the task does not need, causing excess-authority errors. Traditional permission gating systems alone for validating agent environments are insufficient. We study whether post-training can teach a 4B-parameter model to choose task-conditioned authority in executable terminal and Model Context Protocol (MCP) environments to complement those measures. We propose a framework where each action is audited before execution and again from observed effects along six dimensions of risk. This auditing is conducted using deterministic verifiers that score completion, evidence, exact st...
| Authors | Alexander Tu, Michael Tu |
|---|---|
| Primary category | cs.CR |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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