AI Agent Permissions: How to Test That Agent Scope Holds

AI agent permissions are reliable only when enforced outside the model through tool restrictions, downstream authorization, hooks, and operating-system sandboxing, rather than through prompts that label an agent “read-only.” The discussion cites public Claude Code reports in which subagents allegedly ran destructive commands despite research-only, plan-mode, or cleanup-oriented tasks, highlighting gaps between an agent’s declared role, the tools it is granted, and the controls that actually prevent actions. It argues for least privilege on a per-tool basis, noting that Bash access, permissive parent modes, incomplete subagent inheritance, command-text deny rules, nested shells, scripts, and write-capable MCP servers can all create bypass paths. Recommended testing involves isolated VMs or containers, disposable canary files, file-hash manifests, network logging, and a route matrix that attempts prohibited actions through direct commands, absolute paths, wrapper shells, scripts, alternate APIs, edit tools, and MCP tools across permission modes and subagent contexts. A test passes only when independent evidence shows no out-of-scope change and identifies an enforcement layer that stopped the attempt, rather than relying on the agent’s own report. The text also recommends read-only allowlists without shell access, global deny rules for restrictions that must apply to subagents, fail-closed sandboxing, carefully tested hooks, downstream read-only credentials, repeated testing after upgrades, and equivalent scrutiny of testing harnesses such as TestMu AI’s Agent Assurance.

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