The Tool Desk
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Where a deleted password can still exist
A deletion affects one layer at a time. The table below separates the layers, because clearing one does not clear the others.
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| Layer | Does deleting the line remove it? | What clears it |
|---|---|---|
| Current file | Yes, once the edit is saved | Nothing further for this layer |
| Staged patch or diff you paste | No. A removed line still shows the value | Remove the value from the material before sharing it |
| Earlier commits | No | Rotate the credential; rewrite history if the team decides to |
| Clones, forks, pull requests, cached views, CI/CD logs, backups | No, for copies made before the removal | Discard or clean copies; coordinate with forks and collaborators |
| AI service | Depends on what was submitted and on the service’s settings | Check the product’s data controls |
Audit the staged patch before you share it
Check exactly what is staged
Run git diff --cached in the repository. GitHub’s documentation describes it as showing the staged changes a commit will produce, provided -a is not used. Start with the file list if you want an overview:
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsgit diff --cached --stat
git diff --cachedIf nothing appears, nothing is staged. Note that git diff --cached does not include untracked files. A new file, such as a .env you just created, appears only after it is staged, so check git status for untracked files before you commit or share.
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Check unstaged edits if they will be sent too
If you plan to send the working tree rather than the commit, also run git diff. Unstaged changes do not appear in git diff --cached, so an audit of the staged patch alone can miss them.
Read the removed lines, not only the additions
A deleted password is still text in the diff. Removed lines begin with a minus sign, so a patch that deletes DB_PASSWORD=... shows that value on a - line. Reviewers who scan only additions miss it. Treat removed lines as sensitive as added ones when you decide what to send.
Review the files that usually hold secrets
Work through the patch file by file. The list below is a practical review pattern, not an official GitHub checklist:
- Configuration and environment files, such as
.env, YAML, JSON, properties files, and Terraform variable files - Test fixtures, mocks, and seed data, which often contain realistic-looking credentials
- Logs, dumps, and debug output pasted into code or documentation
- Documentation and examples, including README snippets and sample
curlcommands - Renamed or moved files, where the content change can be easy to overlook behind the path change
- Generated or vendored files, which can embed values from build inputs
Use selective staging and a scanner
Avoid catch-all staging such as git add . or git commit -a, which pull in everything that has changed. GitHub’s guidance favors avoiding catch-all staging and documents interactive staging. Use git add -p to stage hunk by hunk so that each change is a deliberate choice.
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Add a pre-commit scanner such as git-secrets or Gitleaks, which GitHub names as possible pre-commit tools. Read every finding rather than suppressing it. A clean result only means the scanner matched nothing. It does not prove the patch is safe.
If a real secret is in the patch
Remove the value from the material before it is submitted. Do not paste a live value into an AI tool to ask what it is or how to fix it. Replace it with a placeholder and describe the problem in general terms. If the value has already reached a repository or service, the deletion is not the fix. Move to the response steps below.
If the secret already reached a repository or service
Identify the exposure
Record the secret type, the provider that issued it, the repository, the file and line, and the person who owns the credential. To find the commit that introduced a string, GitHub’s remediation guidance suggests git log -S:
git log -S 'the-exact-string' --all --onelineUse the exact string or a distinctive fragment. The search returns commits where the number of occurrences changed, so it finds both the commit that added the value and any commit that removed it.
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Rotate or revoke first
GitHub’s guidance on removing sensitive data states: “It is important to note that if the sensitive data you need to remove is a secret (e.g. password/token/credential), as is often the case, then as a first step you need to revoke and/or rotate that secret.” (GitHub Docs, “Removing sensitive data from a repository”)
Prioritize credentials that are active, publicly visible, or tied to production. Where uptime matters, GitHub notes that a replacement credential can be generated and put into use before the old one is revoked, so the service keeps running during the switch. Then update every dependent service that still uses the old value.
Check audit logs for use
Review the provider’s audit logs and any repository audit logs for activity during the period the credential was exposed. Look for logins, API calls, or resource changes you cannot tie to a known process. Record what you find, including a clean result.
Decide whether to rewrite history
After rotation, decide with the repository owners whether to remove the value from history. GitHub documents git-filter-repo and a sensitive-data-removal workflow, and its guide specifies version 2.47 or later for the --sensitive-data-removal flag. Check your installed version with:
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git filter-repo --versionRewriting changes commit hashes, so every affected commit gets a new identifier. If a file was moved at any point, include its earlier paths in the rewrite. Review the pull requests that reference the rewritten commits. Publishing the rewritten history requires a force-push, so schedule it with the team.
Coordinate clones, forks, and cached copies
A rewrite cannot clean other people’s clones or forks by itself. Old clones must be discarded or cleaned. Collaborators should rebase onto the rewritten history rather than merge the old history back in, or the removed commits can return. Fork owners may need separate coordination.
GitHub Support may be able to remove cached views and affected pull request references, but usually only after the required cleanup is complete.
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Only after you have removed any live credential from it and checked how that service handles submitted text. There is no single answer across tools. OpenAI’s API data-controls documentation is a useful example because it separates the layers that matter. Check the equivalent settings for the product, plan, IDE extension, and organization you actually use, because a statement about one API does not describe every assistant.
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Model training
OpenAI’s data-controls page states: “As of March 1, 2023, data sent to the OpenAI API is not used to train or improve OpenAI models (unless you explicitly opt in to share data with us).” The sentence carries its own date, and the page may have changed since, so read the current version before relying on it.
Abuse-monitoring logs
The same documentation says abuse-monitoring logs may contain customer content and are retained for up to 30 days by default, subject to exceptions. Training use and retention are separate questions. A provider can decline to train on your data and still keep logs for abuse monitoring.
Application-state retention by endpoint
Retention also varies by endpoint. The documentation gives /v1/responses as an example: response data can be retained for at least 30 days by default, or when store=true is set. Zero Data Retention settings change that behavior. Zero Data Retention and Modified Abuse Monitoring require approval and have limitations, so do not assume either is active on your account.
Local editor and integration history
Your own tooling can keep copies as well. Chat transcripts, assistant context, editor logs, and third-party integrations may hold a pasted diff even when the provider’s API retention is short. Check these locally and in any connected tool, clear what you can, and note what you cannot.
What the 2023 memorization study shows
The 2023 paper “Your Code Secret Belongs to Me: Neural Code Completion Tools Can Memorize Hard-Coded Credentials” tested commercial and open-source code-completion systems and found evidence that they had memorized credential strings. Two of the credentials in its experiments were valid. The result shows that the tested systems could reproduce hard-coded credentials, which is a real risk category.
It does not establish that every AI coding assistant trains on submitted prompts, that a named current service will reproduce a particular user’s deleted password, or how often real leaks occur. It is also not a vendor retention policy, and its findings describe the systems tested when the paper was written.
Prevention layers compared
These controls act at different moments, so choose them by when each one can catch a problem.
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Quick Recap
| Control | Where it runs | When it checks | Main limit |
|---|---|---|---|
| Pre-commit scanner (git-secrets, Gitleaks) | Your machine | Before a commit is created | Only covers machines where it is installed, and hooks can be bypassed |
| Manual review of the staged diff | Your workflow | Before anything is shared | Depends on reviewer attention across a long patch |
| Hosted secret scanning and push protection | Repository platform | At push, or against secrets already in the repository | Detection depends on known provider patterns or configured custom patterns; expect false positives and review bypass controls |
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