The Tool Desk
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Set permissions by capability and impact
Decide separately what the skill may read, change, send, or execute. A useful starting point is least privilege: expose the smallest capability set that can complete the assigned task. OWASP recommends minimum task-specific tools, per-tool scopes, separate tool sets for different trust levels, and explicit authorization for sensitive operations in its AI Agent Security Cheat Sheet.
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| Capability | Sensible starting scope | Tighten or require approval when |
|---|---|---|
| Files | Read only task-relevant files; allow writes only in an assigned workspace. | The task involves secrets, personal data, system files, or changes outside that workspace. |
| Shell or code execution | Disable unless required; if enabled, use isolated compute with explicit filesystem and network limits. | Commands could affect production, install untrusted packages, delete data, or reach sensitive services. |
| Network | Deny by default where practical; allow only necessary destinations. | A destination could receive private data or issue privileged operations. |
| APIs and tools | Expose only the operations and resources needed; use read scopes where possible. | A call sends messages, changes account state or permissions, makes purchases, or deletes data. |
| Credentials | Avoid raw, long-lived credentials; use scoped, preferably short-lived credentials through a broker where available. | A credential grants access beyond the task or its trust boundary. |
| Memory and user data | Scope data by user and task; minimize sensitive retention. | Data could persist across users, sessions, or future agent runs. |
This is a general baseline, not a universal configuration: actual permission names and controls vary by runtime.
Enforce permissions outside the model
A skill description can tell an agent what it should do, but it does not prevent the agent from attempting other actions. The execution layer should check the requesting actor, tool, target, and parameters against policy each time. OWASP cautions that classifying an action does not itself authorize it; the runtime must still verify permission for the exact action.
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Prefer a narrowly defined tool operation over a general-purpose shell, broad filesystem access, or an unrestricted API credential. Separate read and write scopes, limit writes to specific resources, and keep tools for different trust levels apart. Treat unknown tools or unclassified actions as requiring review rather than assuming they are safe.
Contain execution and restrict network access
Isolation limits what a skill can reach if its behavior goes wrong, but a sandbox does not automatically mean that network access is safe. OpenAI notes that agent-generated code can access files, credentials, and network resources available to its environment, and recommends isolated workloads and outbound traffic limited to approved endpoints in its sandbox security guidance.
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Google says its managed agents run in OS-isolated sandboxes, but outbound network access is unrestricted by default unless an allowlist is configured. Its Agents overview, last updated 2026-09-17 UTC, also recommends limiting tool scopes and reviewing outputs before sensitive use. These are platform-specific details, not defaults that can be assumed for other runtimes.
- Constrain filesystem access to the task’s required resources.
- Configure egress explicitly; allow only destinations the task needs.
- Do not let workloads that should be isolated share access to data or services.
- Recheck containment when tools, data, task scope, or runtime changes.
Keep credentials behind a boundary
Do not treat a secret injected into an agent’s environment as protected from code the agent runs. OpenAI warns that agent-generated code can access environment files and credentials. Where feasible, keep application keys outside the environment and route third-party access through a trusted proxy or server that supplies access only for approved destinations.
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Use credentials with the narrowest scope and lifetime the task permits. Google recommends least-privilege service accounts or API keys and short-lived tokens. If a credential would let the skill reach systems beyond its assignment, do not expose that credential directly to it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Require meaningful approval for high-impact actions
Separate proposing an action from carrying it out. Destructive, financial, administrative, or externally visible actions should require explicit authorization, with an independent check of the action’s target, parameters, scope, and approval state at execution time. Where supported, bind approval to the exact action and make it time-limited; a general “go ahead” should not authorize a materially different operation.
Prompts alone are a weak boundary if users see them constantly. Anthropic reports that roughly 93% of Claude Code permission prompts were approved in its telemetry, and warns that repeated prompts can reduce attention. It also reports an 84% reduction in permission prompts after introducing an OS-level sandbox in Claude Code. These are Anthropic’s product telemetry and implementation result, not universal behavior or independent security benchmarks. See How we contain Claude across products.
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More access can make a task possible or convenient, but it also increases what a mistaken or misused capability can affect. A practical decision is to grant a capability only when its task benefit outweighs the additional reach, and then narrow its target and duration. For example, a task that needs to summarize files may require read access to a specified folder, but not general shell access, writes, or access to unrelated network destinations.
OWASP’s Agentic Skills Top 10 (version 1.0-2026) treats skills as an execution layer shaping which resources agents can access and how they orchestrate multi-step workflows. The permission boundary therefore depends on what the platform actually exposes: an instruction bundle, executable workflow, tool wrapper, or broader runtime extension may need different controls.
Verify changes before relying on them
Review generated code, data transformations, and configuration changes before deployment, especially when they modify data or interact with external systems. Check that the result matches the intended target and scope; do not treat a successful tool call as proof that its effects are correct. Revisit permissions when the task, tools, data, or runtime changes.
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