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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsPutting an API key in a vault does not hide it from a coding agent if the runtime then injects the raw value into an environment the agent can inspect. The reliable boundary is architectural: the credential stays in an application, trusted proxy or server that makes the authenticated call and returns only the result. Everything else (isolation, egress limits, narrow tools, least-privilege keys) reduces damage but does not make a readable secret unreadable.
The specifics below come from current OpenAI and GitHub documentation (accessed 2026-10-05). They describe those platforms, not every agent, IDE, CLI or MCP server, so check your own runtime.
The rule: if the environment can read it, the agent can read it
OpenAI’s sandbox security documentation puts it plainly: “Agent-generated code can access the files, credentials, and network available to its environment.” Treat any credential readable inside the execution environment as accessible to the code the agent writes and runs. That includes environment variables, files on disk, and anything printed to logs or tool output that the agent later sees. (OpenAI, Sandbox security)
The same page warns that injecting a stored secret into the environment still exposes it. A secrets manager protects the secret at rest and in transit to the runtime; it is not by itself a barrier between the secret and the agent. To keep a raw credential inaccessible, broker access outside the environment.
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Keep the raw credential outside: three brokering patterns
Function tools that hold the key in your application
The agent calls a tool such as “create_ticket” with arguments. Your application, which holds the key, performs the request and returns only the outcome. The model sees the operation and its result, never the header. This is the simplest strong pattern when you control the application loop. (OpenAI)
A trusted proxy with placeholders
For third-party calls from an OpenAI-hosted sandbox, OpenAI describes vault secrets exposed as environment-variable placeholders, with a network proxy substituting the real secret for approved hosts. For self-hosted environments, the documentation says the operator must configure a trusted proxy or server. A placeholder protects the secret only as long as the runtime and proxy preserve that boundary, so do not assume the words “vault secret” settle the question. (OpenAI, Vaults)
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A server you operate
Put the authenticated integration behind your own service and give the agent a narrow endpoint. The agent can ask for a constrained operation; it never receives the underlying secret.
Using MCP without exposing your token
OpenAI’s Agents API documents three ways to authenticate MCP connections, and they differ in where the secret is reachable. (OpenAI, MCP connections)
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| Method | How it works | Exposure to code in the environment |
|---|---|---|
| HTTP credential for one session | Pass authorization or headers when creating the session | OpenAI says values are encrypted and omitted from the returned session resource |
| Reusable HTTP credential | Store credentials in a vault and attach it to the MCP connection; matched to the server URL | Applies to connections originating from OpenAI; secret values are not returned when retrieving the vault credential |
| Stdio credential | Provide values in the environment and name them in transport.env_vars |
Documented as readable by code running in the environment |
If the connection originates from the environment rather than from OpenAI, vault credentials do not apply. Use inline authentication or a trusted proxy instead. Server-side credential storage is not the same as protection from code running beside the credential.
MCP itself is described in the Agents SDK documentation as “an open protocol that standardizes how applications provide context to LLMs.” It standardizes the connection, not the secrecy of what you hand it. (OpenAI Agents SDK)
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Where the secret lives: a comparison
| Pattern | Raw credential resides | Implication |
|---|---|---|
| In source, prompts, command text or logs | In material the agent may inspect or retain | High risk; avoid |
| Environment variable in the agent’s environment | Inside the execution environment | Agent-generated code can read it; convenient, not a boundary |
| HTTP MCP session header | Session transport configuration | Documented as omitted from returned session resources; runtime and connection origin still matter |
| Vault-backed MCP credential | Vault attached to a supported OpenAI-origin connection | Value not returned on retrieval; suits reusable credentials |
| Proxy or server brokers the call | Outside the agent environment | Agent requests an operation, never receives the secret |
| Secret scan | Scanning service | Detects some exposed material; not a boundary |
Limit the blast radius
Assume some credential will eventually be reachable, and shrink what it can do.
- Isolate workloads. Use separate environments for work that must not share data, and a dedicated project per application or workload where appropriate.
- Restrict egress. Allow outbound traffic only to endpoints the task needs, so a leaked value is harder to send anywhere.
- Keep the application API key outside the executor. OpenAI describes an executor key limited to connecting environments; code in the environment can read it, so its narrow role is the protection, not secrecy.
- Use least-privilege credentials, rotate them regularly, keep long-lived ones in a secrets manager, and revoke immediately if exposure is suspected.
- Trust only MCP servers you vet, restrict the tools exposed where possible, and require approval for sensitive operations.
- Never put tokens in URLs. Use authorization fields or headers.
Sources: OpenAI sandbox security, MCP connections, Agents SDK MCP.
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Add secret scanning as a safety net
GitHub documents scanning for secrets through its remote MCP server, usable by compatible clients and agents. It requires GitHub Secret Protection and a connected GitHub MCP server. Findings are ephemeral to the active session and are not persisted as GitHub alerts; GitHub describes the scan as a pre-commit safety check, not a system of record. Run it before committing, and keep your normal repository secret scanning and incident response in place. (GitHub Docs)
GitHub custom agents: configuration is not isolation
GitHub custom-agent MCP configuration can take values from organization- or repository-level Agents secrets and variables. Supported forms include $NAME, ${NAME} and ${NAME:-default} in configuration, and ${{ secrets.NAME }} or ${{ vars.NAME }} in custom-agent YAML. This shows how to supply values, not that every consuming runtime stops the agent reading them. Confirm how your runtime handles process environments, session files, tool output and logs before relying on it. (GitHub Docs)
Quick Recap
Quick checklist
- Can code in the agent’s environment read the value (env var, file, log)? If yes, treat it as exposed.
- Can a function tool, proxy or your own server make the call instead?
- For MCP, which transport are you using, and where does the connection originate?
- Is the credential scoped to the minimum, rotatable and revocable?
- Is egress limited and are sensitive operations gated by approval?
- Do you scan before commit?
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