Flock License Plate Readers Escalate Over Data-Entry Typo

A reporter testing a Range Rover for The Drive was surrounded by four armed police cars in a Minnesota Kohl's parking lot in late June after Flock's automated license plate reader (ALPR) system flagged the vehicle as stolen. The stop became confrontational, with officers drawing weapons and demanding to know if the reporter was armed before detaining both him and his wife for an hour.

The stolen vehicle alert traced back to a data-entry error made 2,000 miles away at a Los Angeles fleet management operation. A clerical mistake—entering "34 DTM" instead of the correct "34 03 DTM"—created a mismatched license plate record in Flock's nationwide surveillance network. This single typo caused the system to track five unrelated New Jersey vehicles as they traveled through Minnesota, flagging them as stolen property.

The source of the initial error was a mislabeled license plate from a photo shoot. The Range Rover being tested had never been stolen; the plate designation had simply been recorded incorrectly during a photographic session. Despite the vehicle being part of a legitimate media loan program with documented ownership and proper manufacturer plates, Flock's AI system and law enforcement protocols had no mechanism to catch or correct such edge-case data discrepancies before escalating to armed police intervention.

The incident illustrates how errors in widespread surveillance infrastructure compound across enforcement networks. Once flagged in Flock's system, the vehicle became subject to continuous tracking by cameras mounted on traffic lights and other infrastructure nationwide, with no apparent safeguards to verify data accuracy before police deployment or to de-escalate when circumstances didn't match the alert profile.