AI Agent Credential Check: A Security Checklist

Short answer: an AI agent credential check is a recurring audit of every token, API key, and secret an agent can use - what each one opens, how broad its scope is, when it was last rotated, and how fast you can revoke it. Agents act when nobody is watching, so a stale, broad-scope credential is not a configuration detail. It is an unverified input to every system the agent can call.

Why Agent Credentials Are a Different Problem

A human employee with a leaked password is a bad afternoon. An agent with a leaked token is a bad afternoon that repeats every fifteen minutes until someone notices. Agents hold credentials to act on your behalf: API keys for model providers, OAuth tokens for mail and calendars, connection strings for databases, MCP server tokens for every vertical system they touch. The count grows with each integration, and unlike a person's logins, nobody re-enters them daily or notices when one stops being needed.

The security community has caught up to this. OWASP's Agentic Security Initiative published its first threat-model reference for agentic systems in February 2025, covering threats that did not exist in classical application security: agents that chain tools, retain memory, and act autonomously across trust boundaries. Google's Secure AI Framework (SAIF) makes the same point at the architecture level: AI systems need their own security posture, not a reuse of the perimeter model. The common thread is that agent access has to be treated as a first-class attack surface, and credentials are the most concrete part of that surface.

What the Agent Tooling Already Does

It is worth knowing what your tooling handles for you, because the checklist below covers what it does not. Anthropic's Claude Code security documentation describes several concrete controls. On a developer machine, API keys and tokens are stored in the macOS Keychain when available, in a file with mode 0600 on Linux, and in a file that inherits the user's profile access controls on Windows. In Anthropic-hosted cloud sessions, GitHub credentials are stored encrypted on Anthropic's servers and never enter the session VM at all: the VM holds a short-lived credential scoped to that session, and an Anthropic proxy attaches the real credential on the server side. Remote sessions use multiple short-lived, narrowly scoped credentials, each expiring independently, to limit the blast radius of any single compromise.

The permission layer matters just as much as storage. Claude Code's permission system gates what the agent may do by tool type - reads, bash commands, file writes, web fetches - with approval prompts for anything that changes state. Its documentation even calls out a subtle credential leak: a read-only command pointing at a Windows network path can silently send your Windows credentials to that host, so the tool prompts before allowing it.

These are good defaults. They protect credentials inside one tool. They do not tell you which tokens exist across your whole agent setup, whether last quarter's experiment still has production access, or whether an injected instruction just made your agent use a legitimate token for an illegitimate purpose. That is the part you own.

The AI Agent Credential Check

Run this checklist on every agent you operate, weekly for anything touching production data. The interactive credential check turns this list into ten scored questions with a prioritized fix list; it runs entirely in the browser and never asks for a secret value.

Inventory. List every credential the agent can use, including the ones inside tool configurations: model API keys, OAuth tokens, MCP server tokens, webhook secrets, database strings, and any key sitting in a .env file the agent can read. If you cannot produce the list from memory, that is the finding.

Scope. For each credential, ask what it can open and whether it should. A reporting agent does not need a write-scoped mail token. An agent that reads one spreadsheet does not need drive-wide access. Prefer the vendor's narrowest scope, and where the tool supports it, prefer short-lived credentials over static keys - the cloud-session model above exists precisely because a one-hour token is a smaller prize than a one-year key.

Storage. Check where each secret actually lives. Keys in the OS keychain or a secrets manager are fine. Keys in a git-tracked file, a shared note, or a chat message are not; rotate them immediately, because anything that touched those places must be treated as exposed. On your own machines, the standard is the one the vendors use: keychain-backed storage, or at minimum a 0600 file outside any repository.

Rotation and expiry. Every credential gets an owner and a rotation date. Long-lived tokens that nobody rotates are the ones that outlive the project they were created for. When a pilot ends, its credentials die with it - the experiment's MCP token should not still work six months later.

Revocation drill. Pick one credential and actually revoke it. Time how long it takes you to find where it is set, issue a replacement, and redeploy. If the answer is "I would have to figure that out," the drill just paid for itself. A credential you cannot revoke quickly is a credential you do not control.

Injection exposure. Finally, check what untrusted content the agent reads while holding each credential. An agent that reads incoming mail, web pages, or shared documents with a production token in hand is one crafted message away from using that token on an attacker's behalf. The AI agent verification piece covers the output side of this; the credential check covers the access side. Both are weekly work, which is why I run scheduled monitoring on my own agents instead of trusting memory.

Making It Stick

A checklist that lives in a wiki dies in a wiki. The version that works is the one wired into how you already operate. Put the credential inventory in the same configuration the agent reads - the CLAUDE.md checker validates that configuration, and keeping access notes next to it means the audit trail updates when the setup does. Tie the review to your existing agent reliability routine rather than inventing a new calendar event. And if you are still building your first agent workflows, the AI builder role guide covers where credentials fit in the broader skill set: designing tools and permissions is ordinary API design with a less forgiving caller.

None of this requires new tooling. It requires a list, an owner, a rotation date, and the honesty to run the revocation drill before you need it.

Sources

• OWASP Agentic Security Initiative, "Agentic AI - Threats and Mitigations" (February 17, 2025): https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/
• Anthropic, Claude Code security documentation: https://code.claude.com/docs/en/security
• Anthropic, Claude Code permissions documentation: https://code.claude.com/docs/en/permissions
• Google, Secure AI Framework (SAIF): https://saif.google

FAQ

What is an AI agent credential check?

It is a recurring audit of every token, API key, and secret an agent can use: what each one opens, how broad its scope is, when it was last rotated, and how quickly it can be revoked. The goal is that no credential exists without an owner, a scope, and an expiry plan.

How often should I audit agent credentials?

Weekly for any agent touching production data, monthly for experiments, and immediately when a pilot ends or a team member leaves. The rotation date matters more than the calendar: every credential should have one written down when it is created.

Do agent frameworks store my API keys safely?

The good ones do their part. Claude Code, for example, stores API keys in the macOS Keychain or a 0600 file, and its cloud sessions keep GitHub credentials on Anthropic's servers entirely, issuing only short-lived scoped tokens to the session VM. That protects keys inside the tool; it does not replace your inventory, scoping, and rotation across tools.

What is the biggest credential mistake with AI agents?

Scope creep combined with no rotation: a broad-scope, long-lived token created for a quick experiment that quietly becomes permanent. The second biggest is keeping secrets in places the agent itself can read, like a git-tracked .env file, which turns prompt injection into credential theft.

How does prompt injection relate to credentials?

An injected instruction cannot steal what the agent cannot reach. If the agent reads untrusted content - mail, web pages, shared docs - while holding a powerful token, the attacker's goal shifts from the model to the token. Narrow scopes and short lifetimes are the direct mitigations, which is why the credential check asks what the agent reads, not only what it holds.