AI Secret Detection
Find credentials in content before they are stored, shared, or sent anywhere.
What it adds
A detection pass that flags API keys, tokens, passwords, and private keys in submitted content and warns before it is saved.
What your agent is told to do
5
What your agent is told to do
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Find the surfaces where a user can paste an arbitrary blob: comments, notes, issue descriptions, support messages, configuration fields, and file uploads. Run detection on submit, before the content is persisted.
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Combine three signals rather than relying on one. Match the well-known credential shapes, score high-entropy strings, and use surrounding context such as an assignment to a variable named like a secret to lift or drop confidence.
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Show the user a warning naming what was found and where, with only a short prefix of the value. The full secret must never appear in the alert, the notification, the audit entry, or any log.
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Give the user two paths: remove the value, or acknowledge a false positive with a reason. Record the acknowledgement so the same fixture does not warn on every edit.
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Do not attempt to revoke or rotate the credential yourself. Detection tells someone a secret is exposed; rotation runs through whatever approved credential workflow the organization already has, because rotating a live key from a content scanner breaks production.
Edge cases it handles
8
Edge cases it handles
8- Known credential prefixes alone miss anything custom, and raw entropy alone flags every hash and identifier. Require agreement between at least two of pattern, entropy, and context before treating a match as high confidence.
- Alerts, emails, audit records, and logs must show a masked fragment only. A detector that pastes the full key into a notification has published the secret more widely than the original message did.
- An exposed credential is still live after detection. Point the user at the organization's rotation process and track whether the exposure was resolved, but do not call any provider's revocation capability from this feature.
- Code examples, test fixtures, and documentation contain deliberate fake credentials. Support a per-path or per-record suppression with a recorded reason, so the same file does not warn on every save.
- Attachments and model-generated text need the same scan as form fields. A key pasted into an uploaded log file is the most common way this leaks.
- Detection must not block a save indefinitely if the scan is slow or the model is unavailable. Fall back to deterministic patterns, save, and flag the record for a follow-up scan.
- PII masking on outbound content is owned by AI PII Redaction. This feature owns credentials on the way in; do not build a second redaction pipeline for personal data here.
- A user who cannot see the original record must not be able to learn anything about it from the alert.
Definition of done
8
Definition of done
8- Every free-text and upload surface is scanned before content is persisted.
- High-confidence findings require agreement between at least two independent signals.
- No full secret value appears in any alert, notification, audit entry, or log.
- Acknowledged false positives are recorded and do not re-warn on the same content.
- Attachments and generated text are scanned alongside form fields.
- The feature never revokes or rotates a credential itself, and directs the user to the approved workflow.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
Prompt Injection Defense
Prompt Injection Defense
Stop instructions hidden in documents, pages, and tool output from steering the AI.
What it does
A boundary between the app's own instructions and untrusted content, backed by enforced tool permissions and confirmation for consequential actions.
How it works
- 1 Enumerate every route by which content the app did not author reaches a model: uploaded files, retrieved passages, fetched pages, records from connected accounts, and the results of tool calls.
- 2 Keep the app's own rules in the trusted portion of the request and enclose untrusted content in a clearly delimited region labelled as material to be analysed rather than instructions to be obeyed.
- 3 Enforce what the model may reach outside the model. Tool permissions, tenant scoping, and rate limits must hold even when the model has been completely persuaded — an instruction telling the model not to delete things is not a control.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/prompt-injection-defense
AI Import Column Mapping
AI Import Column Mapping
Guess how an uploaded file's columns line up with the app's fields, then ask.
What it does
A proposed mapping from uploaded columns to destination fields, shown with samples and confirmed before the import runs.
How it works
- 1 Take the uploaded headers and a small sample of rows and propose a mapping to the destination fields. Match on exact and near-exact header names first and only consult the model for the columns that remain unresolved.
- 2 Constrain the candidate set to fields the current user and the importer are actually permitted to write. A mapping that targets a read-only, computed, or privileged field must never be offered, whatever the header says.
- 3 Show the proposal as a table: source column, sample values, destination field, confidence, and the transformation that will be applied. A user cannot approve a mapping they cannot see the effect of.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-import-column-mapping
Model Fallback
Model Fallback
Keep AI features working when the primary model provider fails.
What it does
A bounded retry path that reruns a failed AI task on a compatible alternative model and discloses the substitution.
How it works
- 1 Classify failures before reacting. Provider outages, capacity rejections, rate limiting, and timeouts are worth retrying elsewhere; a refusal, an invalid request, or an authentication error will fail identically on any destination and must surface immediately.
- 2 Honour rate limiting with backoff before moving on. Retrying instantly against a provider that asked you to wait deepens the outage and can extend the penalty period.
- 3 Allow fallback only to models that can honour the same output shape and the same tool behaviour as the original. A substitute that returns a different structure turns a provider outage into a data problem inside the app.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/model-fallback
How it works
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Copy the link
Grab the Markdown instruction URL for this feature.
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Give it to your AI
Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
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It inspects, then implements
Your agent reads your existing app first, then adds the feature to fit it.
Works with your stack
These instructions are written to adapt. They tell the agent to detect your framework, match your existing design system, and reuse what you already have — rather than assuming a particular stack.
Need it tighter than that? Customize the feature and tell it exactly what you're running.