confidence_boost and the manufacturer-data-length signal applied without
any identifying indicator match, giving every device a phantom AirTag
baseline (a 22+ byte payload from any vendor scored 0.30 and was flagged
as an AirTag). Both now require a matched indicator, mirroring the
score>0 gating already used in _check_generic_tracker_indicators.
Name-pattern weight raised 0.15 -> 0.30 so a device advertising a known
tracker name yields a LOW-confidence detection, consistent with the
TSCM BLE scanner's name-only detection and the engine docstring.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Fix SSE fanout thread AttributeError when source queue is None during
interpreter shutdown by snapshotting to local variable with null guard
- Fix branded "i" logo rendering oversized on first page load (FOUC) by
adding inline width/height to SVG elements across 10 templates
- Bump version to 2.26.0 in config.py, pyproject.toml, and CHANGELOG.md
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Implement reliable tracker detection for AirTag, Tile, Samsung SmartTag,
and other BLE trackers based on manufacturer data patterns, service UUIDs,
and advertising payload analysis.
Key changes:
- Add TrackerSignatureEngine with signatures for major tracker brands
- Device fingerprinting to track devices across MAC randomization
- Suspicious presence heuristics (persistence, following patterns)
- New API endpoints: /api/bluetooth/trackers, /diagnostics
- UI updates with tracker badges, confidence, and evidence display
- TSCM integration updated to use v2 tracker detection data
- Unit tests and smoke test scripts for validation
Detection is heuristic-based with confidence scoring (high/medium/low)
and evidence transparency. Backwards compatible with existing APIs.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
James Smith