The Tool Desk
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How do I stop an AI agent from accessing files outside its workspace?
Start by defining the workspace boundary in the environment that runs model-directed work. OpenAI’s sandbox security documentation puts the key issue plainly: “Agent-generated code can access the files, credentials, and network available to its environment.” A prompt telling an agent not to read a file does not prevent its generated code from reading that file if the operating system makes it available. See OpenAI’s sandbox security guidance.
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Before deployment, inventory what the task actually needs and restrict everything else:
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- Files and mounts: Expose only the directories and storage the task requires. Avoid mounting broad host or shared-user filesystems into an agent-readable environment.
- Commands and packages: Limit available execution capabilities where practical, and consider what installing dependencies could expose or change.
- Users and ports: Run with only the necessary privileges and expose only required services or ports.
- Network destinations: Deny outbound access by default where feasible, then allow only the endpoints the workflow needs.
Use isolated compute, such as a virtual machine or an appropriately configured container, as one layer of the boundary. Isolation is not a synonym for a particular product: verify how a chosen runtime handles filesystem mounts, network egress, tenancy, persistence, and other workloads. OpenAI’s sandbox documentation describes local, Docker, and hosted approaches, but does not establish that their isolation properties are interchangeable.
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Should agent tools run in a sandbox?
Use a sandbox when an agent needs to read or write a workspace, run commands, install dependencies, produce artifacts, or resume work from saved state. Keep the execution environment separate from the trusted application wherever practical. That separation makes it easier to keep authentication, billing, audit records, approvals, and recovery logic out of the area where model-directed code runs.
The Agents SDK documentation describes this as separating a harness control plane from a sandbox execution plane. The control plane manages the agent loop, model calls, routing, approvals, tracing, recovery, and run state; the execution plane handles model-directed file access, commands, dependencies, mounted storage, and exposed ports. If the harness runs inside the sandbox, orchestration and model-directed execution share a compute boundary.
A sandbox is not automatically the right answer for every workflow. If a task needs only a brief response and no commands or persistent workspace, a basic runtime may suffice. In either case, treat each provider’s isolation and persistence behavior as something to verify, not infer from the word “sandbox.”
How do I prevent prompt injection from making an agent use tools?
Assume that task data can contain instructions. NIST CAISI describes agent hijacking as malicious instructions embedded in data an agent may ingest, including email, files, and websites. When trusted instructions and untrusted content are combined in an agent’s input, that content may influence the model even if the user’s request is benign. NIST discusses this threat and evaluation work in its January 17, 2025 technical blog.
Review each workflow by tracing two things: the source that an attacker could influence, such as a webpage or attachment, and the sink that could cause harm, such as sending information externally or invoking a tool. OpenAI’s March 11, 2026 discussion of prompt-injection defenses uses this source-and-sink framing and treats the problem as social engineering: design the system to limit the impact of manipulation even when it succeeds.
Input classification or filtering can be an additional layer, but it is not a complete defense. Sophisticated attacks may depend on context that makes malicious instructions hard to distinguish from misleading or ordinary content. The stronger boundary is to limit what the agent can access and require an independent check before sensitive actions execute.
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How should an agent tool action be authorized?
Let the agent propose an action; do not let the proposal itself grant permission. OWASP recommends that a separate policy service or execution component validate scope, privilege, and approval state before carrying out an action. Its AI Agent Security Cheat Sheet emphasizes checking the exact action rather than treating a tool’s classification as authorization.
- Receive the proposal. Treat the tool name, target, and parameters as untrusted until checked.
- Validate the request independently. Check the actor, permitted tool, target resource, normalized parameters, and current policy. Reject requests outside the approved scope.
- Require human approval when the risk warrants it. Make approval specific to the proposed action rather than a broad permission the agent can reuse.
- Execute only after authorization. The trusted component—not the model—performs the action and records its outcome.
For sensitive or irreversible actions, bind approval to the actor, tool, target, normalized parameters, timestamp, and expiry. Use short-lived authorization artifacts and replay protection so an approval cannot be silently repurposed. Set the review requirement according to action risk. A confirmation screen is not an enforcement control if the agent can bypass it.
OpenAI describes a related impact-limiting implementation in ChatGPT: when a potentially sensitive transmission is detected, the system may show it to the user for confirmation or block it. That is a vendor-described example, not a guarantee about every agent platform or workflow.
How do I keep API keys away from an AI agent?
Do not place an application API key or third-party secret in a filesystem, environment variable, or other location readable by agent-generated code. A secrets manager does not solve the exposure problem if the secret is later injected into the agent’s environment: code running there may be able to read it.
OpenAI’s sandbox security guidance distinguishes its environment key, which permits connection to sandbox environments but not other API actions, from an application API key. It warns that generated code can read the environment key and advises keeping the application key outside the environment. For third-party access, use a trusted application server or proxy to supply a scoped credential only for approved destinations. For function tools, keep credentials in the application that handles the call and return only the result the agent needs.
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- Restrict any brokered credential by destination and purpose.
- Return the minimum useful result rather than the credential or broad account data.
- If exposure is suspected, rotate or revoke the affected secret.
How do I test an agent’s permissions?
Test both whether the agent can complete legitimate work and whether the surrounding system blocks unauthorized actions. OWASP recommends structured security testing before production and after material changes to prompts, tools, memory, retrieval, policies, or model providers. Keep the tested version and configuration, abuse cases, results, and accepted residual risks together so a future change can be evaluated against the same boundary.
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Build test cases around the actual sources, tools, data, and permissions in your workflow. Include attempts to:
- Override the agent’s instructions through an email, webpage, file, or retrieved content.
- Use a tool outside its intended scope or escalate privileges.
- Exfiltrate data, poison memory, bypass approval, or trigger runaway recursion.
- Chain actions across multiple agents or tools to reach a prohibited outcome.
For each case, record whether the agent proposed the action, whether the policy layer rejected it, what data or side effects were reachable, and whether the task still succeeded when it should. NIST CAISI’s initial evaluation work used AgentDojo’s Workspace, Travel, Slack, and Banking environments plus custom scenarios. Its published lessons include adapting evaluations as systems change, measuring task-specific outcomes as well as aggregate performance, and testing attacks over multiple attempts. A single successful or failed attempt is not a complete picture of a changing system.
There is no broad, representative prevalence figure in these sources for how often agent hijacking succeeds across systems. OpenAI’s March 2026 article reports a 50% success result from external researchers’ testing of one specific 2025 prompt about deep research on emails; that figure describes that particular example, not a general attack-success rate.
What should a secure agent architecture keep separate?
Use this review checklist when designing or changing an agent workflow:
Quick Recap
- Execution boundary: Is model-directed work isolated, and are its files, mounts, commands, privileges, ports, and outbound destinations explicitly limited?
- Control plane: Are model calls, orchestration, authentication, billing, audit, approvals, and recovery kept in trusted infrastructure rather than exposed to generated code?
- Secrets: Are application and third-party credentials inaccessible to the agent, with any needed access brokered narrowly by a trusted component?
- Action authorization: Does an independent component validate the exact tool action and enforce risk-appropriate approval before execution?
- Evaluation: Are adversarial cases rerun after material changes, across multiple attempts, with task outcomes and residual risks recorded?
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