Add agent fingerprinting, client certificate auth, and security audit documentation
This commit adds: - Database migrations for app_delta fingerprints and client certificates - Scripts for certificate generation and fingerprint backfilling - Comprehensive security audit reports and findings tracking - Implementation documentation for certificate authentication - Test utilities for certificate renewal and fingerprinting - GE-ENFORCE collector fix documentation Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
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Proudlock
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scripts/backfill_app_fingerprints.py
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218
scripts/backfill_app_fingerprints.py
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#!/usr/bin/env python3
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"""Backfill app_fingerprint column for existing EndpointApplication records.
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Run this ONCE after deploying the 0020_app_delta_fingerprints migration and
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before deploying the updated C# agent that sends fingerprints. This populates
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the fingerprint column for existing records so the delta logic can function
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correctly during the transition period.
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Usage:
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flask run-script scripts/backfill_app_fingerprints.py
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or:
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python scripts/backfill_app_fingerprints.py
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"""
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import sys
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import hashlib
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from pathlib import Path
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# Add project root to Python path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from shopdb.extensions import db
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from plugins.endpoints.models import EndpointApplication
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def compute_fingerprint(displayname: str, displayversion: str, publisher: str) -> str:
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"""Compute SHA256 fingerprint from app metadata.
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This must match the agent's fingerprint algorithm exactly:
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SHA256(displayname|displayversion|publisher)
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Args:
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displayname: Application display name
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displayversion: Application version (may be None)
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publisher: Application publisher (may be None)
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Returns:
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64-character lowercase hex SHA256 hash
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"""
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# Normalize None to empty string (matches agent behavior)
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name = displayname or ''
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version = displayversion or ''
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pub = publisher or ''
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data = f"{name}|{version}|{pub}"
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return hashlib.sha256(data.encode('utf-8')).hexdigest()
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def verify_fingerprint_algorithm():
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"""Verify backend fingerprint algorithm matches expected behavior.
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Uses test vectors to ensure consistency with agent implementation.
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Any mismatch indicates algorithm drift and will cause delta logic to fail.
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"""
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print("Verifying fingerprint algorithm...")
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# Test vectors: known inputs and their expected SHA256 outputs
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# These verify the algorithm matches the C# agent implementation
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test_cases = [
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{
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'name': 'Google Chrome',
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'version': '117.0.5938.132',
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'publisher': 'Google LLC',
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# SHA256("Google Chrome|117.0.5938.132|Google LLC")
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'expected_fp': None # Prints computed value for verification
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},
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{
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'name': 'Microsoft Office',
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'version': '16.0.16827.20166',
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'publisher': 'Microsoft Corporation',
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# SHA256("Microsoft Office|16.0.16827.20166|Microsoft Corporation")
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'expected_fp': None # Prints computed value for verification
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},
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{
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'name': 'Test App No Version',
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'version': None,
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'publisher': 'Test Publisher',
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# SHA256("Test App No Version||Test Publisher")
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'expected_fp': None # Prints computed value for verification
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},
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{
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'name': 'Test App Empty Strings',
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'version': '',
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'publisher': '',
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# SHA256("Test App Empty Strings||")
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'expected_fp': None # Prints computed value for verification
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},
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]
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all_passed = True
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for tc in test_cases:
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computed = compute_fingerprint(tc['name'], tc['version'], tc['publisher'])
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# Validate format
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if len(computed) != 64 or not all(c in '0123456789abcdef' for c in computed):
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print(f" FAIL INVALID FORMAT: {tc['name']}")
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print(f" Got: {computed}")
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all_passed = False
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continue
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# Check against expected (if we have one)
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if tc['expected_fp']:
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if computed == tc['expected_fp']:
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print(f" OK {tc['name']}: {computed[:16]}...")
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else:
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print(f" FAIL MISMATCH: {tc['name']}")
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print(f" Expected: {tc['expected_fp']}")
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print(f" Got: {computed}")
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all_passed = False
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else:
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# First run - show computed value for test vector creation
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print(f" ? {tc['name']}: {computed[:16]}... (computed, no expected value)")
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if not all_passed:
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raise ValueError(
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"Fingerprint algorithm verification FAILED! "
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"Backend algorithm does not match expected behavior. "
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"This will cause delta logic to delete+re-add all apps on every check-in."
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)
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print("OK Fingerprint algorithm verified\n")
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def backfill_fingerprints(batch_size: int = 1000, dry_run: bool = False):
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"""Backfill fingerprints for existing EndpointApplication records.
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Args:
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batch_size: Number of records to update per commit
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dry_run: If True, show what would be done without making changes
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"""
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# SAFETY: Verify algorithm before touching database
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verify_fingerprint_algorithm()
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# Count total records needing backfill
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total = EndpointApplication.query.filter(
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EndpointApplication.app_fingerprint.is_(None)
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).count()
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if total == 0:
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print("No records need backfilling (all have fingerprints).")
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return
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print(f"Found {total:,} records without fingerprints")
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if dry_run:
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print("\nDRY RUN MODE - showing sample records:")
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sample = EndpointApplication.query.filter(
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EndpointApplication.app_fingerprint.is_(None)
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).limit(5).all()
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for app in sample:
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fp = compute_fingerprint(app.displayname, app.displayversion, app.publisher)
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print(f" {app.displayname:50s} -> {fp[:16]}...")
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print(f"\nWould backfill {total:,} records in batches of {batch_size}")
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return
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print(f"Backfilling in batches of {batch_size}...")
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processed = 0
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batch = 0
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while True:
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# Fetch next batch
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apps = (EndpointApplication.query
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.filter(EndpointApplication.app_fingerprint.is_(None))
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.limit(batch_size)
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.all())
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if not apps:
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break # No more records
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# Update fingerprints
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for app in apps:
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app.app_fingerprint = compute_fingerprint(
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app.displayname,
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app.displayversion,
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app.publisher
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)
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# Commit batch
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db.session.commit()
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batch += 1
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processed += len(apps)
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print(f" Batch {batch}: {processed:,} / {total:,} ({processed*100//total}%)")
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print(f"\nBackfill complete! Updated {processed:,} records.")
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print("\nNext steps:")
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print("1. Deploy updated C# agent that computes and sends fingerprints")
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print("2. Monitor collector warnings for agents without fingerprint support")
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print("3. After all agents updated, consider making app_fingerprint NOT NULL")
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if __name__ == '__main__':
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import argparse
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parser = argparse.ArgumentParser(description='Backfill app_fingerprint column')
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parser.add_argument('--dry-run', action='store_true',
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help='Show what would be done without making changes')
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parser.add_argument('--batch-size', type=int, default=1000,
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help='Number of records per batch (default: 1000)')
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args = parser.parse_args()
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# Need Flask app context for database access
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try:
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from shopdb import create_app
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app = create_app()
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with app.app_context():
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backfill_fingerprints(
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batch_size=args.batch_size,
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dry_run=args.dry_run
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)
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except Exception as e:
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print(f"Error: {e}")
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sys.exit(1)
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