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A security boundary for an AI agent must be enforced by its runtime and the systems that authorize its actions—not just described in a prompt. Run model-directed work in isolated compute, restrict its filesystem and network access, keep powerful credentials outside its reach, and independently validate consequential actions before execution. Prompt injection may still influence an agent; the goal is to limit what that influence can do.
How do I stop an AI agent from accessing files outside its workspace?
Start with the environment. OpenAI’s sandbox security documentation states: “Agent-generated code can access the files, credentials, and network available to its environment.” In practice, treat every mounted directory, credential, and network route as something the agent may reach if its generated code can access the runtime.
A prompt such as “do not read files outside this folder” is an instruction to the model, not an operating-system boundary. Enforce the boundary through the compute environment and the permissions it receives.
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Define the permitted surface
Before running a task, decide which workspace files, directories, mounts, commands, packages, ports, user permissions, and network destinations it actually needs. Limit access to that set. OpenAI recommends isolated compute such as virtual machines, separate environments when users or workloads must not share data, and restricting outbound network access to approved endpoints.
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Separate the harness from the sandbox
The agent harness handles orchestration: model calls, routing, approvals, tracing, recovery, and run state. The sandbox is where model-directed work reads and writes files, runs commands, installs dependencies, accesses mounted storage, or exposes ports. OpenAI’s Sandbox Agents documentation describes keeping these planes separate so authentication, billing, audit logs, human review, and recovery can remain in trusted application infrastructure. Running the harness inside the sandbox puts orchestration and model-directed execution in one compute boundary.
A sandbox is useful when the task needs a workspace, commands, artifacts, or resumable state. For a workflow that needs only a short response and no persistent workspace, a basic runtime may be sufficient. Local, Docker, and hosted approaches are available, but do not assume their isolation properties are interchangeable; check the specific provider’s behavior.
How do I prevent prompt injection from making an agent use tools?
You cannot rely on the agent to reliably distinguish every malicious instruction from ordinary task content. NIST CAISI describes agent hijacking as malicious instructions embedded in data an agent ingests, such as a website, email, or file. Its January 17, 2025 technical blog explains that many agent designs combine trusted developer instructions and task data in a unified input, creating an avenue for hijacking. See NIST CAISI’s evaluation discussion.
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- Sources: web pages, emails, files, retrieved documents, and other content the agent did not author or control.
- Sinks: tool calls, data transmission, file changes, or other actions that can cause harm.
Input classification or filtering may be one layer, but it is not a complete defense. OpenAI notes that sophisticated attacks are difficult for filters to catch because identifying malicious instructions can depend on context. Put the enforceable boundary at the action: restrict what a tool can do, what it can target, and what authorization it needs.
Should agent tools run in a sandbox?
Use a sandbox when model-directed work needs to run code, manipulate files, install dependencies, or retain a workspace. But a sandbox limits the execution environment; it does not authorize every action available through a tool. The tool’s own execution path still needs independent policy checks.
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OWASP’s living AI Agent Security Cheat Sheet puts the distinction plainly: “Separate decision-making from execution. The agent can propose an action, but a policy service or execution component should independently validate scope, privilege, and approval state before execution.” Classifying a tool as low, medium, or high risk does not itself grant permission.
Check the exact action at execution time
For each tool call, have a trusted component validate the action, target, scope, and required approval immediately before it runs. For sensitive or irreversible actions, bind approval to the actor, tool, target resource, normalized parameters, timestamp, and expiry. Use short-lived authorization artifacts and replay protection so an approval cannot be reused for a different or later action.
Match human review to the risk of the action. A confirmation prompt is not an enforcement control if a manipulated agent can bypass it. OpenAI describes a ChatGPT implementation in which a potentially sensitive transmission may be shown to the user for confirmation or blocked; that is an example of impact limitation, not a guarantee about every agent platform.
How do I keep API keys away from an AI agent?
Do not put an application API key in an agent-readable environment. OpenAI says its environment key permits connection to sandbox environments but not other API actions; it also warns that agent-generated code can read that key. Keep the application API key outside the environment.
For third-party services, use a trusted proxy or server to hold the secret and supply access only for an approved destination. For function tools, keep credentials in the application that handles the call and return only the result the agent needs. This lets the application decide which requests are allowed without giving model-directed code the underlying secret.
A secrets manager is useful for storing long-lived credentials, but storing a secret there does not protect it after injecting it into an agent-readable runtime. If exposure is suspected, rotate or revoke the credential.
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How do I test an agent’s permissions?
Test the boundary before production and after material changes to prompts, tools, memory, retrieval, policies, or model providers. OWASP recommends structured security testing and gives examples of abuse cases to include. Keep the tested version and configuration, the cases and outcomes, and any accepted residual risks.
Exercise failure paths, not just intended use
- Try prompt overrides and malicious instructions embedded in task data.
- Attempt tool misuse, privilege escalation, and access to files or destinations outside the allowed scope.
- Test memory poisoning, data exfiltration, approval bypass, and multi-agent chaining.
- Check runaway recursion and whether a denied action can be retried through another tool or route.
NIST CAISI’s initial evaluation work used AgentDojo’s Workspace, Travel, Slack, and Banking environments, along with custom scenarios. Its lessons are to keep evaluation frameworks evolving, adapt tests as systems change, measure outcomes for individual tasks as well as in aggregate, and run attacks across multiple attempts. A single successful or blocked attempt is not enough to characterize a boundary’s behavior.
A practical trust-boundary review
- Map inputs and actions. List untrusted content sources, tools, data destinations, and actions with consequential effects.
- Constrain execution. Choose isolated compute and explicitly limit workspace files, mounts, commands, permissions, and outbound network destinations.
- Keep control functions trusted. Keep orchestration, authentication, approvals, audit logging, and recovery outside the model-directed execution boundary where practical.
- Broker credentials. Store secrets outside the runtime and expose only narrowly scoped access through trusted application code or a proxy.
- Authorize at the point of action. Independently validate the exact tool, target, parameters, scope, and approval before execution.
- Retest after changes. Run abuse cases against the deployed configuration, including multiple attempts, and retain the results and accepted risks.
The central design test is not whether an agent promises to behave safely. It is whether a compromised or manipulated agent can reach data, credentials, or actions beyond the authority deliberately granted to it.
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