Secrets in Prompts: Detecting and Neutralizing Credentials Before They Leak

Principle: Secrets are not like PII. You can’t redact-and-restore them safely; you must block and rotate. Treat any detected secret as an incident—even in dev. This article shows how to architect secret detection at the AI boundary, eradicate legacy leaks in your estate, and build a rotation pipeline so incidents resolve in minutes, not days.

Where secrets sneak in

Copy/paste convenience: Engineers paste a failing curl command (with bearer token) into an AI chat for help.
Console output & logs: Verbose debug prints a JWT; tail -f gets pasted into a prompt.
Misplaced files: .env or kubeconfig opened in a browser tab and copied piecemeal.
Browser autofill & clipboards: Password managers and sync features move secrets between contexts.
Generated code: LLM drafts config with hardcoded secrets that get reused in prompts for troubleshooting.

The boundary: inline secret scanners with hard blocks

Install scanners in your AI gateway/SDK. On any suspected secret, reject the request, log a structured incident, and return a friendly error that explains next steps. Do not forward masked versions; do not store; do not try to restore later.

Detection methods (layered)

High-precision regex: Known prefixes/patterns (e.g., AKIA[0-9A-Z]{16} for AWS access keys; Slack tokens like xox[baprs]-; GitHub PAT ghp_[A-Za-z0-9]{36}).
Entropy heuristics: Long high-entropy strings with base64/hex URL-safe alphabet near trigger words ("token", "secret", "key").
Context windows: Words like Authorization:, Bearer, ssh-rsa, BEGIN PRIVATE KEY.
ML classifiers (optional): Catch vendor-specific formats and obfuscated variants.

Combine methods to reduce false positives; require two signals (pattern + context or entropy + context) for block in ambiguous cases.

Response automation: from detection to rotation

Quarantine the text in-memory only; never write to disk. Emit an event: {requestId, detector, suspectedVendor, confidence, userId, route}.
Triage lambda classifies vendor (AWS, GCP, GitHub, Slack, Stripe, internal). If confidence is high, trigger rotation runner.
Rotation runner calls provider APIs or internal KMS to revoke/rotate the credential. For SSH keys, flip authorized_keys; for DB creds, create and migrate to a new user/password.
Notify owner via chat/email with remediation details and links to the diff.
Forensics checks logs and repos for the same token; runs a short-term search across knowledge bases and tickets.

Estate cleanup: hunt existing leaks

Even with a gateway, old leaks remain. Run periodic scans across:

Repos & wikis: Server-side scanning (pre-receive) and scheduled full-history scans with tools like trufflehog/gitleaks-class equivalents.
Tickets & chat exports: Use your secret detectors against export dumps. Quarantine and re-sanitize.
Object stores: Audit buckets for text blobs containing triggers; expire public links; set bucket policies that reject uploads matching secret patterns.

Observability hygiene (make leaks impossible)

Logging schemas: Only IDs, enums, counts; no raw strings. Enforce in code review with static analysis; in runtime with schema validators.
Error trackers: Drop messages containing high-entropy strings or secret patterns. Replace with event IDs and links to internal traces.
Analytics events: Runtime guards that reject payloads with suspect fields; dashboards that show secret detection counts per app/route.

Developer experience: make the safe path faster

Ship a paved SDK with built-in scanners and helpful errors that link to a Secrets 101 page. Offer a Copy Redacted button in your UI that automatically strips tokens from curl commands, config snippets, and headers.

Policy-as-code (opinionated defaults)

{"entity":"SECRET","action":"block","restore":false}
{"entity":"TOKEN","action":"block","restore":false}
{"entity":"PASSWORD","action":"block","restore":false}

Secrets are never placeholders. Don’t allow exceptions in production. In dev sandboxes, still block but allow a short-lived "explain mode" that tells engineers how to rotate and attach sanitized examples.

Testing and metrics

Seeded corpora: Include real-format but fake tokens (check-digit/format-valid) for AWS, GCP, GitHub, Slack, Stripe, DB creds, SSH keys. Expect 100% detection.
False positives: Base64 data URLs, UUIDs, and hashes; tune thresholds and require context words.
KPIs: Time to rotate (goal: < 10 minutes), leaked secrets in observability (goal: zero), detection coverage across routes, repeat offenders by team for targeted training.

Incident playbook (copy to your wiki)

Contain: Block request; halt related pipelines; revoke credentials.
Assess: Determine exposure window and access attempts.
Eradicate: Remove from repos, wikis, tickets, and buckets; invalidate caches.
Recover: Confirm systems on new creds; monitor for anomalies.
Lessons: Patch policies, add test cases, and follow up training.

FAQ

Q: Can we allow masked secrets for debugging? A: No. Even masked patterns teach attackers about format and presence. Provide synthetic examples instead.

Q: Are JWTs always secrets? A: Treat active JWTs as secrets; expired JWTs should still be scrubbed from prompts/logs to avoid confusion and mimicry.

Q: What about tokenized PAN or vault references? A: Still sensitive. Block in prompts and prefer placeholders that describe role (e.g., <PAN_TOKEN#1>).

The bottom line

Secrets handling is binary: block and rotate. With inline detection, rotation pipelines, and leak-proof observability, you can turn scary incidents into fast, boring routines—and keep credentials out of models, logs, and screenshots for good.