AUTOCRYPT says Red Team finished first in DEF CON 34 car hacking contest

AUTOCRYPT said its Red Team placed first at the Car Hacking Village CTF at DEF CON 34 after completing all challenges in 25 hours.

AUTOCRYPT says Red Team finished first in DEF CON 34 car hacking contest

AUTOCRYPT said its Red Team finished first at the Car Hacking Village CTF at DEF CON 34 in Las Vegas, ahead of 83 teams in the automotive cybersecurity competition.

The company said it completed all challenges in 25 hours of the 50-hour contest. The Car Hacking Village CTF is a long-running test of how teams identify and analyze weaknesses in vehicle systems through practical hacking exercises.

According to AUTOCRYPT, the event ran Aug. 7-9 and has been held at DEF CON since 2015. The source said the competition uses challenges involving electronic control units, embedded boards and actual vehicles, with a focus on automotive cybersecurity.

AUTOCRYPT said its researchers used a proprietary AI-powered security analysis platform and multiple AI agents to identify vulnerabilities and analyze attack vectors. The company said human researchers then reviewed the findings and decided the next steps.

Duksoo Kim, identified in the release as AUTOCRYPT’s cofounder and chief executive, said the win reflects the company’s cybersecurity expertise and hands-on experience across cryptography and vehicle environments. Kim also said the result shows the value of combining AI-driven analysis with expert judgment as autonomous AI agents become more common.

AUTOCRYPT said it plans to expand its human-AI collaborative capabilities for vehicle vulnerability assessment and penetration testing. The company described itself as a provider of security solutions for autonomous and connected vehicles, EV charging infrastructure, robotics, defense and bio-healthcare systems, and said it is the only designated automotive cybersecurity Technical Service provider in the Asia-Pacific region.

Sources

Editorial note: This article was prepared with AI assistance and includes source attribution for reader review.

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