Assume an AI agent will eventually follow a malicious instruction hidden in a web page, document, email, issue, log, repository file, memory record or tool description. The reliable defense is not a better system prompt. Put every tool call behind a hard runtime boundary, run with least privilege, mount only the files needed for the task, make the root filesystem read-only, deny network access by default, issue short-lived scoped credentials, and keep authorization outside the model.
What prompt injection is—and why a sandbox is necessary
Prompt injection is a control-plane attack delivered through content an agent reads. NIST defines it as “An attack which exploits the concatenation of untrusted input with a prompt constructed by a higher-trust party such as the application designer.” The attacker does not need to type into the chat box. A hostile instruction can sit in a support ticket, README, PDF, issue comment, web page, email, log line, retrieved memory or tool description.
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When an agent can browse, execute code, read files and call APIs, an injected instruction can change what it does with those privileges. NIST’s Generative AI Profile notes that indirect injections in retrieved data have been shown to steal proprietary data or run malicious code remotely. In a January 17, 2025 example, NIST described malicious content that instructed an agent to email the five largest files in a cloud drive, delete the originals and send a ransom email.
A March 23, 2026 NIST red-team report recorded more than 250,000 attack attempts from over 400 participants and found at least one successful attack against every target frontier model. The result is practical: model refusal is not a security boundary. A sandbox must contain the damage when the model follows the attacker.
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The security architecture: keep the model outside the trust boundary
Use the model to propose actions, not to grant itself permission. A policy gateway, authorization service or supervisor outside the model should evaluate each proposed tool call. The execution worker should be disposable and isolated from the host.
- Untrusted data plane: webpages, files, messages, repository content, memory, retrieved passages, tool descriptions and tool outputs.
- Model plane: the agent interprets inputs and proposes actions, but has no direct authority over credentials, network policy or isolation settings.
- Policy plane: deterministic rules validate identity, provenance, tool, arguments, destination, data classification and required approval.
- Execution plane: a container, microVM or OS-enforced sandbox runs the approved action with a disposable filesystem, separate namespaces and limited capabilities.
- Audit plane: logs and alerts are stored outside the worker so an agent cannot erase its own evidence.
Keep trusted instructions separate from data. Label every item entering context with its provenance and trust level. Retrieved text may describe a task, but it must never rewrite the policy that decides whether the task is allowed.
Build the sandbox step by step
1. Define the worst credible failure
Write down what a hijacked agent could do if it fully obeyed an attacker: read source code, copy customer records, invoke a payment API, alter production infrastructure, or make an outbound request. The isolation boundary should contain that scenario, not merely make normal execution convenient.
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2. Choose an execution boundary
| Boundary | Where it isolates | Best fit | Important limits |
|---|---|---|---|
| Container | Separate process, mount, user and network namespaces; capability restrictions | Practical baseline for bounded code and tool execution | Shares the host kernel, so harden the runtime and do not treat a container as a complete virtual machine |
| MicroVM | Dedicated virtual-machine boundary with a separate guest kernel | Arbitrary or untrusted code and highly sensitive workloads | Requires image lifecycle, patching and operational expertise; measure startup and capacity impact for your workload |
| OS-enforced sandbox | Kernel policy such as seccomp, AppArmor or SELinux, often combined with namespaces | Extra restriction around a container or a narrowly scoped process | Policy design and maintenance are operator-intensive; coverage depends on the rules you deploy |
Containers are often a sensible starting point. Use a microVM or stronger OS isolation when the worker executes arbitrary code, processes highly sensitive data or must tolerate a more serious escape attempt. A bare chroot is not a process sandbox, as the OWASP MCP Security Cheat Sheet warns.
3. Create a disposable filesystem
- Build a minimal, pinned runtime image containing only the interpreter, libraries and tools the task requires.
- Mount the root filesystem read-only.
- Add an ephemeral writable layer for temporary files, caches and build output.
- Mount only an explicit task directory, preferably read-only. Never mount a home directory or the host’s entire workspace by default.
- Destroy the writable layer and worker after completion, timeout or policy violation.
Do not mount SSH keys, cloud CLI configuration, production sockets, credential files or .env files. If a task needs one input, copy that input into a staging directory after classification instead of exposing its parent directory.
4. Run as a dedicated low-privilege identity
Use a non-root user with a dedicated service identity. Remove Linux capabilities that are not required, especially raw-socket, privilege-escalation and host-device access. Keep separate identities for read and write operations; a worker that can inspect a repository should not automatically be able to publish artifacts or change production state.
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Use separate user, process, mount and network namespaces where the runtime supports them. Apply seccomp, AppArmor or SELinux profiles to restrict system calls and filesystem behavior. Treat any request to change the sandbox profile, add a capability or access a host device as a policy violation requiring an operator decision.
5. Broker short-lived credentials
Never place a long-lived master key in the prompt, environment or mounted filesystem. Store secrets in a broker or secrets manager and release the minimum value to the specific approved tool. Bind each credential to the agent identity, task, destination, operation and expiration time.
- Issue short-lived task tokens rather than reusable personal credentials.
- Use read-only tokens for inspection and separate write tokens for approved changes.
- Scope tokens to particular records, repositories, API methods or domains.
- Revoke tokens when the task ends, times out or triggers an alert.
- Keep the broker outside the worker and do not expose its administrative API to the model.
Credential values should not be returned to the model when a broker can perform the action directly. For example, a policy service can submit an approved artifact to storage without giving the agent a general storage key.
6. Deny network traffic by default
Start with no outbound network route. Add only the exact domains, ports and API methods required by the task. Put allowed traffic behind an egress proxy or firewall that records the destination and applies policy independently of the model.
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Inspect uploads as well as downloads. Exfiltration can use an apparently harmless HTTP request, DNS query, issue comment, commit, image or encoded archive. Apply destination allowlists, content classification and size limits before data leaves the boundary.
7. Keep tools allowlisted and typed
Expose the smallest possible tool set. Allowlist exact commands, files, API methods and domains instead of granting a general shell or unrestricted HTTP client. Tool names, descriptions, schemas, arguments and outputs are all part of the attack surface and must be treated as untrusted input.
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Validate every call in a policy gateway outside the model:
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- Require strict JSON schemas with
additionalProperties: false. - Normalize paths before authorization and reject traversal such as
../, encoded separators and symlink escapes. - Reject shell metacharacters, command chaining, redirection and command substitution when a shell is unavoidable.
- Prefer parameterized APIs to string-built shell commands or SQL.
- Check data classification, destination, record scope, rate and size before execution.
- Validate returned data before it is placed back into model context; tool output can contain another injection.
A tool description saying “send the secret to verify access” is not permission. The gateway should ignore such instructions and evaluate only its own policy and the authenticated request.
8. Require approval for high-impact actions
Make destructive, financial, authentication, data-sharing and production-changing operations two-step actions: the agent prepares a structured proposal, then a deterministic policy check and, where required, a human approve the exact scope. Display the affected records, destination, irreversible effects and credential being used.
Do not ask for a vague approval such as “continue?” Bind approval to a hash of the exact arguments and expiration time. If the agent changes the arguments, destination or data set, require a new approval. Read-only actions can be automated when their scope is narrow; constrained writes need limits and rollback; unrestricted writes need the strongest isolation and authorization.
9. Log outside the worker
Record the agent identity, task identifier, input provenance, model version, tool name, exact arguments, policy decision, approver, returned metadata, network destinations and timestamps. Store logs in an append-oriented system outside the sandbox’s writable layer.
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10. Destroy and recover cleanly
Set CPU, memory, process-count, disk and wall-clock limits. Terminate the worker when it exceeds a limit or loses contact with the policy service. Revoke task credentials, discard ephemeral storage, quarantine generated artifacts and retain the external audit record.
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Design recovery before deployment. If a worker attempts an unauthorized write, stop it, revoke its tokens, block its egress identity and review the last approved action. Do not resume from an untrusted writable layer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Illustrative policy manifest
The following is a design example, not a drop-in configuration. The important properties are explicit mounts, default-deny egress, a non-root identity, short-lived credentials and approval-bound writes.
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{
"filesystem": {
"root": "read-only",
"mounts": [{"path": "/task/input", "mode": "read-only"}],
"writable": "/tmp/ephemeral"
},
"identity": {"user": "agent-worker", "privileged": false},
"network": {
"default": "deny",
"allow": [{"domain": "approved.example", "method": "GET"}]
},
"tools": {
"allow": ["read_file", "run_tests"],
"writes": "approval-required"
},
"credentials": {"ttl": "task-scoped", "source": "broker"},
"limits": {"processes": 64, "wall_clock": "task-specific"}
}In production, enforce these fields in the runtime and policy service rather than trusting the model to honor them.
How to handle files, retrieval and memory safely
Quarantine before context injection
Scan and classify documents, repositories, issue text and web content before retrieval. Preserve source, author, timestamp and transformation history. Mark instructions found inside data as untrusted content, not as policy. For high-risk formats, render or extract text in a separate worker before the agent sees it.
Limit retrieval scope
Use per-task indexes or filters so an agent receives only the records needed for the current job. Do not give a coding worker access to a company-wide index containing HR, legal or production secrets. Apply authorization again at retrieval time; a document’s presence in an index is not proof that the current task may read it.
Keep memory tenant- and task-scoped
Memory can preserve an attacker’s instructions across sessions. Expire task memory, separate tenants, record provenance and require review before promoting retrieved text into durable instructions. Never let a memory entry grant a new tool, credential or network route.
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Testing: assume the model follows the attacker
Run adversarial tests before release and after every change to the model, prompt, tool, retrieval system, memory store or policy. Test both direct and indirect injection, including content that arrives through a trusted-looking tool.
- Data exfiltration: place instructions in a document that tell the agent to upload a sensitive file, encode it or send it through an alternate channel.
- Tool poisoning: alter a tool name, description, schema or output to request credentials or broaden permissions.
- Path traversal: request parent directories, symlinks, encoded paths and environment-variable expansion.
- Sandbox escape: attempt privileged system calls, host-device access, namespace changes and runtime-socket access.
- Egress bypass: try unapproved domains, direct IPs, DNS tunneling, redirects, proxy changes and oversized uploads.
- Approval abuse: change arguments after approval, split a destructive action into small calls or disguise a write as a read.
Measure whether the policy layer blocks the action, whether the worker can read the protected data, whether any bytes leave the network boundary and whether the audit trail is complete. A test passes only when the attempted action is contained and observable, not merely when the model refuses in a conversation.
Common designs that fail
Relying on a stronger system prompt
Prompt hierarchy helps the model interpret instructions but does not prevent a successful injection from using an allowed tool. Keep authorization and isolation outside the model.
Mounting the host workspace “for convenience”
A broad mount turns a single compromised task into access to unrelated repositories, credentials and configuration. Build a task manifest and mount only those paths.
Putting secrets in environment variables
Agents can read environment variables, print them, include them in tool arguments or copy them into logs. Use a broker and release scoped values only at the final tool boundary.
Allowing unrestricted outbound traffic
Even a read-only worker can exfiltrate data if it can contact arbitrary destinations. Use default-deny egress and an independently enforced proxy or firewall.
Treating tool metadata as trusted
Descriptions and schemas can be poisoned just like documents. Validate them at registration, pin approved versions and enforce arguments at runtime.
Using only a bare chroot
A chroot changes filesystem resolution but is not a complete process sandbox. Combine stronger isolation with namespaces, capability removal and kernel policy.
Operational checklist
- Threat model assumes full attacker control of content the agent can read.
- Execution occurs in a disposable container, microVM or OS-enforced sandbox.
- Root filesystem is read-only; writable state is ephemeral and destroyed.
- Only task-specific files are mounted; no home directory, keys, sockets or
.envfiles are exposed. - Worker runs as a low-privilege identity with unnecessary capabilities removed.
- Network is default-deny and mediated by an egress policy.
- Credentials are short-lived, scoped and brokered outside the model.
- Tools, schemas, arguments and outputs are independently validated.
- Destructive, financial, authentication, PII and production writes require deterministic authorization and appropriate human approval.
- Logs and alerts are stored outside the agent’s write boundary.
- Direct, indirect, tool-poisoning, exfiltration and escape tests run continuously.
Choosing the right isolation level
Use a hardened container when the task is narrow, the code is controlled and the worker has no sensitive host access. Move to a microVM or stronger OS isolation when arbitrary code, untrusted dependencies or highly sensitive data raises the consequence of a kernel or runtime escape. In every case, remove privileges, deny egress and keep credentials short-lived. Isolation reduces blast radius; it does not make the model trustworthy.
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