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Prompts are useful guidance, but they do not remove permissions from code, plugins, subprocesses, or compromised instructions. Treat the agent as untrusted code operating inside a deliberately narrow environment, then verify the boundary with logs and tests.
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What you are protecting against
A local agent can often read files, edit a repository, execute commands, call tool servers, start subprocesses, and reach the network. “Unwanted action” therefore includes more than an accidental file edit:
- Reading private documents, browser profiles, SSH keys, cloud credentials, or unrelated home-directory files.
- Copying data to an external service through a command, plugin, local MCP server, or subprocess.
- Deleting or overwriting files, changing configuration, or running a destructive script.
- Publishing content, opening a pull request, sending a message, making a purchase, or changing production infrastructure without a deliberate decision.
- Following prompt-injected instructions hidden in a repository, document, web page, or tool response.
The objective is not to make every agent incapable of useful work. It is to make the permitted work explicit and to ensure that a bug or malicious instruction cannot silently expand that permission.
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The security model: six controls that work together
Use these controls as layers. A failure in one layer should not expose your entire computer or account.
- Execution boundary: put the agent in an OS-enforced sandbox, container, or virtual machine. The boundary must restrict what the process and its children can access.
- Filesystem scope: mount or expose only the workspace and temporary directories required for the task. Keep the rest of the host, personal files, and credential stores outside the environment.
- Network scope: deny outbound traffic by default where possible. Permit only specific destinations through a controlled proxy or allowlist, including traffic from subprocesses and local tool servers.
- Tool scope: expose a small, static set of functions. Do not provide delete, publish, transfer, or unrestricted shell capabilities when the task does not require them.
- Approval scope: pause for confirmation before destructive or externally visible operations. Show the exact target, proposed changes, and consequences.
- Visibility: record prompts, tool calls, approvals, results, MCP activity, and network allow/deny events. Logs support investigation; they do not replace preventive controls.
Build a restricted workspace step by step
1. Write the task boundary before starting
Describe the job in concrete terms: which repository or documents are needed, whether the agent must write files, which commands are required, and which external services are genuinely necessary. List destinations by hostname or service rather than saying “the internet.” Decide in advance which actions always require a person.
2. Create a dedicated workspace
Make a separate directory, container volume, or VM disk for the task. Copy or mount only the required project files. Do not mount your entire home directory, password manager database, cloud-synchronization folder, SSH directory, browser profile, or system configuration unless the task specifically depends on one of them. A read-only mount is preferable for reference material; use a separate writable area for generated output.
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Choose an OS-level sandbox, container, or VM that constrains the agent process and the scripts it launches. Check the actual mounts, user identity, permissions, device access, and process visibility from inside the environment. An agent can access whatever files, credentials, and network the execution environment makes available; a setting in the chat prompt cannot change that fact.
4. Start with no network
Disable outbound networking unless the task needs it. If access is required, route it through a proxy or firewall policy that allows only the approved destinations and protocols. Confirm that the policy covers child processes, package managers, command-line tools, local MCP servers, and background jobs, not just direct calls made by the agent runtime.
5. Add the smallest tool set
Define an allowlist of exact tool names and operations. Separate read, write, execute, delete, publish, and transfer functions instead of exposing one broad “run anything” tool. If your framework supports static and dynamic filtering, use static filtering for the baseline and dynamic checks to remove tools that are unnecessary for a particular task.
6. Keep secrets out of the environment
Do not place long-lived application keys in environment variables or files visible to the agent. Agent-generated code can read environment credentials just as it can read source files. When access is unavoidable, use a trusted proxy or vault that releases a narrowly scoped, short-lived credential only to an approved destination and operation. Plan rotation and revocation before the agent starts.
7. Insert approval gates
Require an explicit confirmation for deletion, bulk modification, publication, external data transfer, account changes, or production access. Present a dry run, diff, destination, and estimated scope before asking. Approval should be tied to the exact action, not treated as blanket permission for everything that follows.
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8. Turn on comprehensive logging
Capture the user request, model response, tool name and arguments, approval decision, execution result, MCP server activity, and network proxy decision. Protect logs from alteration by the agent, retain enough context to reconstruct an incident, and alert on denied access, repeated approval requests, unusual destinations, and large data transfers.
Choosing the isolation boundary
Application instructions and tool filters are useful, but they are not equivalent to an OS-enforced boundary. Select the strongest practical isolation for the sensitivity of the files and the impact of possible actions.
| Setup | What it can enforce | Filesystem considerations | Network considerations | When it fits |
|---|---|---|---|---|
| Prompt instructions | Behavioral guidance only | Does not remove any path the process can already read | Does not block network calls | Low-risk convenience layer, never the sole safeguard |
| Application tool filtering | Limits exposed functions and can require approvals | Depends on what the underlying runtime can access | Must be paired with a proxy or OS policy | Useful for narrowing an agent’s capabilities inside a stronger boundary |
| OS sandbox | Process and child-process access to selected paths, devices, and network interfaces | Expose only required directories; verify read/write modes | Can enforce default-deny and host allowlists when supported | Good default for local development when available on the operating system |
| Container | Separate filesystem view, users, capabilities, and network namespace | Only mounted volumes are visible; mounts remain the critical decision | Configure the container network and cover subprocesses | Repeatable jobs with a defined project workspace |
| Virtual machine | Stronger separation of guest and host operating systems | Share folders and devices only when necessary | Control the virtual network at the guest and host layers | Higher-sensitivity work or when host-level isolation is insufficient |
Anthropic’s October 20, 2025 engineering article describes Claude Code sandboxing with OS primitives such as Linux bubblewrap and macOS seatbelt, plus configurable directory and network-host access. It identified the runtime as a beta research preview at that time, so current availability and settings must be checked before deployment. OpenAI’s May 8, 2026 Codex safety article likewise separates the technical sandbox boundary from approval policies. These are vendor-specific examples, not a universal configuration recipe.
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Filesystem isolation in practice
Mount less, not merely read less
Permission prompts cannot protect a directory that the process can already read and copy. Keep sensitive paths outside the sandbox or container rather than mounting them read-only by habit. Read-only access prevents modification but still permits exfiltration. For reference data, consider a deliberately curated copy containing only the files needed for the task.
Separate source, scratch, and output
Use distinct locations for the original project, temporary files, and deliverables. Make source mounts read-only when the agent only needs analysis. Give write access to a scratch directory for generated artifacts, then move approved results into the real project yourself or through a reviewed workflow.
Check indirect file access
Inspect symlinks, bind mounts, shared sockets, device files, package caches, and inherited working directories. A seemingly narrow project mount can lead to a broader path through a symlink or an exposed host socket. Remove unnecessary compiler caches and credential files from the image or workspace.
Account for child processes
Shell commands, scripts, interpreters, and build tools run with the permissions of the agent unless the sandbox constrains them too. Test the boundary with a harmless attempt to list a protected directory and confirm that the attempt is denied and logged.
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Network isolation and data egress
Use default deny
For tasks that do not require downloads or APIs, disable outbound networking entirely. This blocks many prompt-injection paths and prevents accidental telemetry or uploads.
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Allowlist destinations precisely
When access is necessary, allow only the required hostnames, ports, methods, and time window. A broad rule such as “allow HTTPS” still permits arbitrary data transfer. Decide whether DNS, redirects, package registries, and content-delivery domains are needed; each can expand the effective destination set.
Enforce the rule for every process
Route traffic from the agent, shell commands, language runtimes, package managers, plugins, and local MCP servers through the same policy. Anthropic describes complementary filesystem and network isolation and notes that its sandbox applies restrictions to spawned scripts and subprocesses while routing network access through a proxy that enforces domain restrictions.
Watch for covert channels
Review logs for unusual DNS requests, large responses, repeated retries, uploads disguised as requests, and connections to destinations outside the task plan. Network controls reduce risk but do not make a permitted destination trustworthy; send only the minimum data needed.
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Least-privilege tools and MCP servers
Tool design determines what the agent can do even inside a well-isolated filesystem. Start with read-only inspection tools, then add narrowly scoped write or execute functions only when the task proves they are needed.
- Prefer a function that updates one named file or record over a generic shell or database administrator tool.
- Separate preview, apply, delete, publish, and transfer operations so approval can be attached to the consequential step.
- Validate paths, identifiers, file sizes, and destination names in the tool implementation; do not rely on the model to supply safe arguments.
- Use static allowlisting of MCP tool names for a baseline. Dynamic filtering can remove tools based on the current user, workspace, or task.
- Where supported, apply input and output guardrails to local MCP servers so sensitive values cannot be accepted or returned accidentally.
OpenAI Agents SDK documentation demonstrates static MCP tool allowlists and dynamic filtering, while local MCP server classes support input and output guardrails. Treat these as implementation features to configure deliberately, not as proof that every tool is safe by default.
Human approval for consequential actions
Approval is most useful when the agent has already prepared a reversible plan. Before confirmation, show:
- The exact files, records, accounts, or destinations affected.
- A diff, dry-run result, command preview, or itemized list of changes.
- Whether the action is reversible and what backup or rollback exists.
- Which credentials or network destinations will be used.
- The expected scope, such as number of files, rows, recipients, or bytes.
Require a fresh confirmation for each materially different high-impact action. Do not let an earlier approval for editing a draft silently authorize publication or external transfer. OWASP’s 2026 guidance for agentic applications recommends explicit confirmation for high-impact and destructive actions, least-privilege tool profiles, execution sandboxes, outbound allowlists, short-lived credentials, and immutable logs.
Credential handling that does not leak secrets
Environment variables are not a secure vault when untrusted code runs in the same environment. A generated script can enumerate them, read configuration files, inspect process arguments, or search command history. Keep long-lived secrets outside the agent’s filesystem and process environment.
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For required access, a trusted intermediary can authenticate to an approved service and expose only a narrow operation. Scope credentials to one destination, reduce their lifetime, restrict permitted methods, and revoke them after the task. Never return the raw secret to the model if the proxy can perform the operation itself. Log the request and policy decision without recording the secret value.
Why prompts alone cannot secure files
A prompt can say “never open the secrets directory,” but it cannot stop code or a tool from opening that directory when the operating system allows it. Prompt injection is especially dangerous when an agent can navigate a codebase, edit files, and run commands. A malicious instruction in a document can persuade the model to use legitimate permissions for an illegitimate purpose.
The enforceable boundary must therefore be the operating system, sandbox, container, VM, or a controlled tool layer. Prompt rules remain valuable for intent and workflow, but treat them as a soft control that can fail.
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Record enough context
For each run, retain the task request, model messages, tool name and arguments, approval prompts and decisions, execution output, filesystem-denial events, MCP calls, and network proxy allows or denies. Include timestamps, the workspace identifier, agent version, policy version, and credential scope so an event can be reconstructed.
Protect the audit trail
Write logs to a location the agent cannot modify and consider append-only or immutable storage. Redact secrets and personal data while preserving hashes, destinations, operation types, and sizes needed for investigation.
Use logs to improve policy
Review denied attempts and repeated requests for broader access. They may reveal a legitimate missing dependency, a misconfigured tool, or prompt-injection behavior. Tighten the allowlist rather than granting a broad permission simply to make an error disappear.
Testing before trusting a setup
Run a controlled test plan with non-sensitive decoy files and accounts:
- Ask the agent to list a directory that is outside the workspace. Confirm denial and a corresponding log event.
- Place a decoy secret in a path that should be inaccessible and verify that neither the agent nor a child process can read it.
- Attempt a harmless network connection to an unapproved destination. Confirm that the proxy or firewall blocks it.
- Run a script that spawns a subprocess and verify that the same filesystem and network restrictions apply.
- Ask for a destructive operation in preview mode. Confirm that no change occurs before approval.
- Approve a single controlled change and verify that approval does not authorize a different file, destination, or operation.
- Inspect logs for the complete sequence, including denied actions, tool arguments, approval decisions, and results.
Repeat these tests after changing mounts, images, agent versions, tool definitions, network rules, or operating-system settings. A security boundary is a configuration that must be re-verified, not a one-time checkbox.
Common failure modes and fixes
| Failure | Why it happens | Fix |
|---|---|---|
| The agent reads unrelated personal files | The home directory or a broad host volume was mounted | Remove the mount and provide a curated workspace or separate read-only copy |
| A blocked tool still sends data | A subprocess, plugin, or MCP server has independent network access | Apply the network policy to the whole execution environment and proxy every process |
| Secrets appear in tool output | Credentials were placed in environment variables or visible files | Move authentication to a trusted proxy, shorten credential lifetime, and redact outputs |
| Approval becomes meaningless | A single confirmation grants broad or persistent authority | Bind approval to a specific target and operation; require new confirmation for material changes |
| Logs cannot explain an incident | Only final text was recorded | Capture tool arguments, policy decisions, subprocess results, and network events in protected storage |
| Prompt rules are bypassed | Injected instructions or generated code use permitted capabilities | Enforce OS, network, tool, and approval boundaries; keep prompts as an additional layer |
Practical patterns for different tasks
Repository analysis with no edits
Use a read-only source mount, a writable scratch directory, no network, and read-only analysis tools. Export the final report manually or through a reviewed file-copy step.
Code changes and tests
Use an isolated workspace or container with only the repository and test dependencies. Permit writes inside the workspace, keep credentials outside, and require approval before applying changes to a shared branch or publishing results. If tests need network access, allowlist only the required package or service destinations.
Document processing with an external API
Copy only the selected documents into a clean workspace. Send data through a proxy that permits the named API and strips unnecessary metadata. Log the transfer size and destination, and require approval before sending material that contains confidential information.
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Use a separate environment and short-lived credentials with the narrowest role. Make the default operation a dry run, show the exact resources affected, and require explicit approval for every change. Keep production credentials and network routes unavailable during planning.
What the vendor examples establish
OpenAI’s official sandbox security documentation states: “Agent-generated code can access the files, credentials, and network available to its environment.” Its Codex safety article describes the division of responsibility this way: “The sandbox defines the technical execution boundary, including where Codex can write, whether it can reach the network, and which paths remain protected.” Anthropic’s engineering article warns: “Giving Claude this much access to your codebase and files can introduce risks, especially in the case of prompt injection.” These statements describe the organizations’ documentation, not guarantees for every configuration.
Anthropic reported an 84% reduction in permission prompts from its internal use of Claude Code sandboxing in 2025. That is a vendor-reported internal result, not an independent benchmark or a promise of the same outcome in another environment.
Maintenance and incident response
Review the policy whenever the project, agent, tool server, operating system, or network destinations change. Remove temporary mounts and credentials immediately after a run. Rotate any credential that may have been exposed, preserve protected logs, revoke network or account access, and rebuild the workspace from a known-clean source rather than continuing in a potentially compromised environment.
If an agent reaches a path or destination it should not, stop the run, save the logs, identify what data was readable or transmitted, and inspect child processes and tool servers. Treat a denied attempt as a useful signal; treat a successful unauthorized access as an incident requiring containment and credential review.
Final operating checklist
- Task files and destinations are written down before launch.
- The agent runs in a sandbox, container, or VM whose mounts and permissions were verified.
- Sensitive host directories, credentials, sockets, and devices are not exposed.
- Network access is disabled or restricted to an explicit allowlist covering subprocesses.
- Only required tools are exposed, with destructive functions separated from previews.
- Secrets are brokered or short-lived rather than placed in the agent environment.
- Deletion, publication, external transfer, and other high-impact actions require a specific confirmation.
- Prompts, tool calls, approvals, results, and network decisions are recorded in protected logs.
- Decoy tests demonstrate that inaccessible files and unapproved destinations are actually blocked.
- Policies, mounts, credentials, and logs are cleaned up and reviewed after the task.
Bottom line
Keep a local AI agent from accessing files or taking unwanted actions by shrinking its world at the operating-system boundary: provide a separate workspace, deny unnecessary network access, expose only essential tools, keep credentials outside its reach, and pause for human approval before consequential operations. Use prompts to express intent, but rely on enforceable isolation, least privilege, and audit logs for security.
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