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Hackers reveal how Flock cameras really track cars and people

Ars Technica · Securityby Dhruv Mehrotra, Joseph Cox, wired.com ()8 min read1
AI summary · glm-5.3-flashSummaries by Vani AI

Hackers tore down a Flock Safety camera, dumped its storage, recovered an encryption key, and revealed its software detects people as well as vehicles.

Hackers from a collective calling itself stegan0gram removed a Flock Safety camera from service, made a near-complete copy of its storage, and recovered an encryption key stored on the device that unlocked thousands of vehicle-detection videos, sharing the files with 404 Media, WIRED, and Distributed Denial of Secrets. Analysis of about 21 days of logs shows more than a million images, roughly 28 photos per passing vehicle, and Flock-built apps that detect people, bicycles, and even bumper stickers, while plate inference happens on Flock's servers. Flock had previously downplayed early-2025 root-access research by Jon Gaines, and records from Alpharetta, Georgia were searchable by over 2,000 agencies via Flock's national network.

Key facts
  • Hackers recovered an encryption key from an unencrypted 'media' partition, unlocking thousands of vehicle-detection videos.
  • Recovered logs show about 21 days of activity and over a million captured images.
  • Device software explicitly detects people, bicycles, and bumper stickers; plate reading happens server-side.
  • Alpharetta, Georgia camera records were accessible to more than 2,000 agencies nationwide.
  • Early 2025 research by Jon 'GainSec' Gaines documented root-level flaws that Flock downplayed.
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From the original article

8 min read at arstechnica.com

One hacked camera captured 1. 6 million images and could detect people as well as cars.

Ars Technica · Security

By Dhruv Mehrotra, Joseph Cox, wired.com · arstechnica.com

Read the full article at Ars Technica · Security

A short excerpt; the article and its images belong to the publisher. Summary, key facts and assessment above are AI-written from the article and can be wrong.