Security model overview
Threat model: we handle other people’s secrets’ metadata. Controls: masks+salted fingerprints, GET-only, SSRF-hardening, double webhook checks, magic-link auth.
We exist because secrets leak; leaking yours through us would be self-refuting.
Controls inventory
- Live values die inside detectSecrets — outputs are masks + salted fingerprints.
- GET/HEAD only, enforced in fetch layer.
- SSRF ranges refused pre-socket + per hop.
- Webhook destinations validated twice.
- Magic-link auth: zero stored passwords.
- Residual risks stated: DNS-rebinding class belongs to egress filtering; rate limits are per-instance.
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 security model masking fingerprints; 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.
Where this fits
This document is part of the support knowledge base that mirrors production behaviour exactly: detector counts, plan limits and payload shapes on these pages are computed from the same catalogs that power the product, and tests assert they cannot drift. If anything here contradicts observed behaviour, report it via the false-positive process — calibration improves fastest when reality disagrees loudly.
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.