Fooling real cars with Deep Learning

The motivation for attacking a vehicle is vast -- it can be hacked to gain personal information (as previously IoT devices were hacked), leak contacts or use its connectivity for DDoS attacks and similar traditional attacks, Newly crafted exotic attacks may be introduced, such as ransomware (and pay to unlock), GPU attack to mine cryptocurrencies (and pay for the electricity), or steering an autonomous truck carrying goods to a desired location, combining Spoofed traffic signs and GPS jamming (without breaking a single line of code). Attacking vehicles' Software 2.0, does not require traditional software hacking skills, nor does it even requires specific automotive technologies knowledge. With a plethora of published research papers (albeit, the vast majority of it is simulative), listed in Nicholas Carlini's homepage (a Google Brain researcher, specializing in adversarial networks), every single researcher is a published neural-nets hacker. Not everyone agrees, though, that adversarial-image-based attacks are a real threat. Chris Valasek & Charlie Miller, the 2015 hacked Jeep Cherokee hackers duo, have addressed in a 2018 paper that autonomous vehicles may be "less hackable than you think".