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Choose a boundary the agent cannot read
The key question is whether agent-generated code can access the real credential. If a secret is injected into an environment the agent can inspect, it is exposed to that code. Storing a value in a secrets manager first does not change that if the application later injects it into the agent-readable environment. OpenAI describes this risk in its agent safety guidance.
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Instead, let the agent request a narrow operation and have trusted application code, a function tool, or an egress proxy attach authentication after the operation is approved. Return a small result object rather than raw authenticated request or response material. This keeps the credential outside the model-visible exchange, but it does not by itself prevent other components from logging sensitive data.
Compare the main credential paths
| Design | Where authentication happens | Can agent code read the credential? | Important limitation |
|---|---|---|---|
| Application-owned function tool or server | In trusted application code, which calls the upstream service and returns the result | Not if the application keeps the credential out of the agent environment and response | The application must protect its own storage, logs, and tool boundary. |
| OpenAI-hosted sandbox with vault-backed environment credential | An OpenAI network proxy substitutes the real secret for a placeholder in an approved HTTPS request | The sandbox receives a placeholder, not the real value | Does not provide credentials to self-hosted environments or application-run function tools; placeholder must be passed unchanged in a supported request. |
| OpenAI MCP over HTTP, session credential | Credential is supplied in session transport configuration | Depends on the connection boundary; the guide says the credential is encrypted and omitted from the returned session resource | Keep it out of reusable definitions and logs. |
| OpenAI MCP over HTTP, vault credential | A matching vault credential is used for connections from OpenAI | Designed to keep reusable credentials out of agent definitions | Applies to the documented OpenAI-origin connection setup. |
| MCP over stdio with environment values | Credential is placed in the process environment | Yes, code running in that environment can read it | Do not treat an environment variable as secret from the tool-server process. |
| MCP HTTP from an environment | Inline authentication or a trusted proxy | Depends on whether the environment can inspect the credential | The documented OpenAI setup does not use vault credentials for environment-origin HTTP. |
These MCP distinctions follow OpenAI’s remote MCP guide; the sandbox behavior is described in its agent safety guidance. For self-hosted agents, provide your own trusted server or proxy boundary rather than assuming an OpenAI-hosted credential feature applies.
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Configure the OpenAI-hosted sandbox credential boundary
In OpenAI’s documented hosted-sandbox pattern, the sandbox request contains a placeholder. A network proxy substitutes the actual secret only for approved hosts. The placeholder mechanism works when passed unchanged in a supported HTTPS request; it cannot reveal the secret for local computation such as request signing. If signing or other local secret-dependent work is required, keep that operation in trusted application code and expose it through a function tool.
Configure connectivity and credential injection as separate controls:
- Network destination policy:
allowed_domainscontrols which hosts the sandbox can reach. - Credential destination policy: credential
allowed_hostscontrols where the proxy may inject the secret. OpenAI specifies exact host names here, without a scheme, path, port, or wildcard. - Transport constraints: the documented proxy supplies credentials only to HTTPS destinations on port 443 or 8443.
- Policy intersection: with restricted network access, the credential host must also be reachable under the sandbox network policy.
A host being reachable does not automatically mean it should receive a credential. Restrict both lists to the destinations the task needs. See the OpenAI sandbox and vault credential guidance for the current platform behavior.
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Keep credentials out of MCP configuration and agent definitions
OpenAI’s MCP guide describes session-scoped HTTP credentials, reusable HTTP credentials stored in a vault for connections from OpenAI, and stdio credentials supplied as environment values. For environment-origin HTTP, the guide calls for inline authentication or a trusted proxy rather than vault credentials. Choose based on where the tool server runs and which code can inspect its process or configuration.
- Do not put a reusable secret in a prompt, agent definition, plugin archive, or source file.
- For stdio, assume code running in the environment can read process environment values.
- For HTTP, use the session or vault option only for the connection mode it supports; use an application-side or proxy boundary when the agent environment must not see the value.
Connection-mode details are in the OpenAI remote MCP guide. A vault is useful only when the secret remains on the trusted side of the boundary instead of being copied into agent-readable configuration.
Redact before data is persisted
Redacting a dashboard display is too late if the unredacted event has already been written to a log store or exported. Inspect every component that handles the request and its errors: agent traces, framework callbacks, tool-server logs, proxy access logs, exception reporting, observability exports, and downstream response handling.
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Unless there is a narrowly controlled diagnostic need, avoid persisting:
- Authorization and proxy-authorization headers or fields
- Credential-bearing URLs
- Full request and response bodies that may contain secrets or sensitive data
- Exception objects or error details that may include headers, URLs, or payloads
Apply redaction at the point logs are created, and verify each component’s actual behavior. A proxy can separate authentication from agent code, but it does not automatically control what the proxy, tool server, framework, or downstream service records.
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Confirm the behavior against the SDK version installed in your application: the guide does not state a package version. If debugging requires payloads, use a controlled environment with restricted access and limited retention, then turn the setting back off. These details are in the OpenAI Agents JS SDK configuration guide.
Use a deployment checklist
- Store: Put the long-lived credential in a controlled application-side store or vault. Do not copy it into prompts, source files, reusable configuration, plugin archives, or diagnostic output.
- Mediate: Expose a narrow operation to the agent. Have trusted code or an approved proxy attach authentication and return only the result needed for the task.
- Constrain: Allow only required tools and destinations. Separately restrict which hosts can receive credential injection; where supported, also scope methods and credential lifetime.
- Review persistence: Check agent traces, framework callbacks, tool-server and proxy logs, exception reporting, observability exports, and downstream response handling. Redact before writing or exporting.
- Verify logging defaults: Keep sensitive model and tool payloads disabled in normal operation. If temporary debugging access is justified, limit who can see the logs and how long they are retained, then disable payload logging again.
- Respond to exposure: Revoke or rotate a suspected leaked credential and audit persisted logs and traces for copies. Later redaction cannot remove a value already sent to another store.
These controls align with the OWASP Securing Agentic Applications Guide 1.0, which recommends isolated agent execution, restricted filesystem and network access, dedicated secret management, credential rotation, and checks that secrets are not written to logs.
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