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Documentation Index

Fetch the complete documentation index at: https://docs.vidocsecurity.com/llms.txt

Use this file to discover all available pages before exploring further.

Learnings are rules Vidoc creates when you ignore issues. They help Vidoc avoid flagging similar false positives in future scans.

How Learnings Work

  1. You find a false positive issue
  2. Click “Ignore” and provide a reason
  3. Vidoc creates a learning from the context
  4. Future scans apply the learning automatically
  5. Similar false positives are filtered out

Creating Effective Learnings

When ignoring an issue, provide clear, specific reasons:
Good ReasonWhy It’s Effective
”Input is sanitized by sanitizeHtml() in middleware”Explains the security control
”This is a test file, not production code”Identifies context
”User input is validated against allowlist”Describes protection mechanism
Better reasons create more accurate learnings. Be specific about why the issue is a false positive.

Viewing Learnings

The Learnings page displays:
  • Learning ID - Unique identifier
  • Reason - Why the original issue was ignored
  • Created - When the learning was created
  • Applied Count - Number of issues this learning affects

Learning Details

Click a learning to see:
  • Original issue that triggered the learning
  • All issues where this learning is applied
  • Full context and code snippets

Managing Learnings

Delete a Learning

If a learning is too broad or no longer valid:
  1. Click the learning
  2. Click “Delete Learning”
  3. Affected issues return to Open status
Deleting a learning may cause previously filtered issues to reappear in future scans.

Review Applied Issues

To see which issues a learning affects:
  1. Click the learning
  2. View the “Applied Issues” section
  3. Review if the learning is correctly applied

Best Practices

  • Review learnings periodically - Ensure they’re still valid
  • Use specific reasons - Vague reasons create imprecise learnings
  • Don’t ignore real issues - Only create learnings for true false positives
  • Check applied count - High counts may indicate overly broad learnings

Issues

Ignore issues to create learnings

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