Revoking an exposed OpenAI key (and what to check first)

Usage evidence, revocation path, project-scoped replacement, spend caps — the OpenAI-specific incident flow.

2026-08-234 min read

What actually happened

Evidence capture costs thirty seconds and answers questions support tickets cannot. The build reported success at every step, which is exactly why nobody stopped it.

Export usage dashboard covering exposure window.
Delete exposed key at platform.openai.com/api-keys.
Issue scoped, capped replacement server-side only.
Verify removal via re-scan; monitor.

Every step above is individually reasonable and none of them prints a warning. The value crosses into the bundle during substitution, not execution, so nothing in your runtime ever sees the moment it happened.

What someone can do with it

Replacement strategy determines recurrence odds:

Unscoped replacements inherit publication risks wholesale.
Caps convert worst-cases into bounded annoyances.

Rotate first

Rotate before you touch any code. The key has been served to every visitor since the deploy that introduced it, cached by CDNs along the way, and very likely harvested by crawlers that scan served JavaScript for exactly these shapes. platform.openai.com/api-keys Removing the string from source does not un-publish it; only revocation closes the door.

A rotated key left in old deploys is still discoverable in CDN caches and archived copies. Rotation plus redeploy closes both halves; either alone leaves the door ajar.

Move the call somewhere the browser cannot read

The structural fix is always the same shape: the call moves to a context that holds the key without serving it, and the browser asks your server instead.

const k = "sk-proj-FAKE…"; // wherever it lived

const k = process.env.OPENAI_API_KEY; // server routes only

Check whether yours is exposed

You can check manually right now: open the site, view source or open DevTools, and search the built JavaScript for eyJ. A hit means the string shipped; decode or prefix-check it before deciding how bad the news is.

Manual searching proves one page on one day. KeyDrift fetches the deployment the way a browser would, follows chunks named only in route manifests, reads streamed hydration payloads, and classifies what it finds — secret, or public-by-design — so an anon key never shows up dressed as an emergency.

Keep it from coming back

One more thing worth knowing before you close the tab: fixing this once does not end the story. Agents imitate whatever pattern is already in the repo, templates carry their own defaults, and the next feature request can reintroduce the same shape of code. Continuous monitoring re-scans every deploy and alerts only on change — new, regressed, resolved — so the comeback attempt is a notification instead of a quarter-end surprise.

Why this failure class persists

Zoom out and the pattern is bigger than one repo. AI-assisted output has outgrown review capacity everywhere at once, which means thousands of teams are making the same reasonable-looking tradeoffs in the same week. Nobody using modern tooling is uniquely exposed. The failure mode documented above is the modal outcome of velocity without verification, not evidence of carelessness.

How KeyDrift reports this exact finding

Report anatomy matters during incidents, so it is worth reading once calmly: masked string (never the live value — it ceases to exist outside the detection engine), salted fingerprint (trackable within your workspace, useless to strangers), chunk path (your starting point for a "git log -S" hunt), disposition (secret versus public-by-design), confidence (matches below 0.5 never reach the page at all).

Manual check, step by step

The full manual drill, for readers who want zero dependence on any tool: open the deployed site in a private window; launch DevTools → Sources; use Search-all-files (Ctrl/Cmd+Shift+F) for eyJ; then repeat for the other marker families — eyJ, sk_live_, sk-proj-, AKIA, postgres, BEGIN PRIVATE KEY. Decode anything JWT-shaped before reacting, and classify public-by-design formats as expected guests rather than intruders.

Close the loop with monitoring

If you take one operational step from this page, make it this: put the URL under continuous monitoring (free tier covers one project daily). The first scan tells you whether you have a problem today; the schedule tells you whether the problem comes back next month after someone re-adds the convenient line.

Common questions

Refund fraudulent usage?

Possible via support with evidence attached — captured in phase one precisely for this.

Org-wide audit needed?

If admin keys were involved: minted-key review precedes closure.

Run a free scan at keydrift.dev/scan — paste a URL or the bundle source itself, no account. Findings arrive masked, with the exact chunk they live in.