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NTSB Investigation of Fatal Driverless Car Accident

Autonomous systems are going to have to do much better than this.

The Uber car that hit and killed Elaine Herzberg in Tempe, Ariz., in March 2018 could not recognize all pedestrians, and was being driven by an operator likely distracted by streaming video, according to documents released by the U.S. National Transportation Safety Board (NTSB) this week.

But while the technical failures and omissions in Uber’s self-driving car program are shocking, the NTSB investigation also highlights safety failures that include the vehicle operator’s lapses, lax corporate governance of the project, and limited public oversight.

The details of what happened in the seconds before the collision are worth reading. They describe a cascading series of issues that led to the collision and the fatality.

As computers continue to become part of things, and affect the world in a direct physical manner, this kind of thing will become even more important.

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License Plate “NULL”

There was a DefCon talk by someone with the vanity plate “NULL.” The California system assigned him every ticket with no license plate: $12,000.

Although the initial $12,000-worth of fines were removed, the private company that administers the database didn’t fix the issue and new NULL tickets are still showing up.

The unanswered question is: now that he has a way to get parking fines removed, can he park anywhere for free?

And this isn’t the first time this sort of thing has happened. Wired has a roundup of people whose license places read things like “NOPLATE,” “NO TAG,” and “XXXXXXX.”

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Modifying a Tesla to Become a Surveillance Platform

From DefCon:

At the Defcon hacker conference today, security researcher Truman Kain debuted what he calls the Surveillance Detection Scout. The DIY computer fits into the middle console of a Tesla Model S or Model 3, plugs into its dashboard USB port, and turns the car’s built-in cameras­ — the same dash and rearview cameras providing a 360-degree view used for Tesla’s Autopilot and Sentry features­ — into a system that spots, tracks, and stores license plates and faces over time. The tool uses open source image recognition software to automatically put an alert on the Tesla’s display and the user’s phone if it repeatedly sees the same license plate. When the car is parked, it can track nearby faces to see which ones repeatedly appear. Kain says the intent is to offer a warning that someone might be preparing to steal the car, tamper with it, or break into the driver’s nearby home.

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Another Attack Against Driverless Cars

In this piece of research, attackers successfully attack a driverless car system — Renault Captur’s “Level 0” autopilot (Level 0 systems advise human drivers but do not directly operate cars) — by following them with drones that project images of fake road signs in 100ms bursts. The time is too short for human perception, but long enough to fool the autopilot’s sensors.

Boing Boing post.

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Adversarial Machine Learning against Tesla’s Autopilot

Researchers have been able to fool Tesla’s autopilot in a variety of ways, including convincing it to drive into oncoming traffic. It requires the placement of stickers on the road.

Abstract: Keen Security Lab has maintained the security research work on Tesla vehicle and shared our research results on Black Hat USA 2017 and 2018 in a row. Based on the ROOT privilege of the APE (Tesla Autopilot ECU, software version 18.6.1), we did some further interesting research work on this module. We analyzed the CAN messaging functions of APE, and successfully got remote control of the steering system in a contact-less way. We used an improved optimization algorithm to generate adversarial examples of the features (autowipers and lane recognition) which make decisions purely based on camera data, and successfully achieved the adversarial example attack in the physical world. In addition, we also found a potential high-risk design weakness of the lane recognition when the vehicle is in Autosteer mode. The whole article is divided into four parts: first a brief introduction of Autopilot, after that we will introduce how to send control commands from APE to control the steering system when the car is driving. In the last two sections, we will introduce the implementation details of the autowipers and lane recognition features, as well as our adversarial example attacking methods in the physical world. In our research, we believe that we made three creative contributions:

  1. We proved that we can remotely gain the root privilege of APE and control the steering system.
  2. We proved that we can disturb the autowipers function by using adversarial examples in the physical world.
  3. We proved that we can mislead the Tesla car into the reverse lane with minor changes on the road.

You can see the stickers in this photo. They’re unobtrusive.

This is machine learning’s big problem, and I think solving it is a lot harder than many believe.

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Zipcar Disruption

This isn’t a security story, but it easily could have been. Last Saturday, Zipcar had a system outage: “an outage experienced by a third party telecommunications vendor disrupted connections between the company’s vehicles and its reservation software.”

That didn’t just mean people couldn’t get cars they reserved. Sometimes is meant they couldn’t get the cars they were already driving to work:

Andrew Jones of Roxbury was stuck on hold with customer service for at least a half-hour while he and his wife waited inside a Zipcar that would not turn back on after they stopped to fill it up with gas.

“We were just waiting and waiting for the call back,” he said.

Customers in other states, including New York, California, and Oregon, reported a similar problem. One user who tweeted about issues with a Zipcar vehicle listed his location as Toronto.

Some, like Jones, stayed with the inoperative cars. Others, including Tina Penman in Portland, Ore., and Heather Reid in Cambridge, abandoned their Zipcar. Penman took an Uber home, while Reid walked from the grocery store back to her apartment.

This is a reliability issue that turns into a safety issue. Systems that touch the direct physical world like this need better fail-safe defaults.

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“Two Stage” BMW Theft Attempt

Modern cars have alarm systems that automatically connect to a remote call center. This makes cars harder to steal, since tripping the alarm causes a quick response. This article describes a theft attempt that tried to neutralize that security system. In the first attack, the thieves just disabled the alarm system and then left. If the owner had not immediately repaired the car, the thieves would have returned the next night and — no longer working under time pressure — stolen the car.

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Gas Pump Hack

This is weird:

Police in Detroit are looking for two suspects who allegedly managed to hack a gas pump and steal over 600 gallons of gasoline, valued at about $1,800. The theft took place in the middle of the day and went on for about 90 minutes, with the gas station attendant unable to thwart the hackers.

The theft, reported by Fox 2 Detroit, took place at around 1pm local time on June 23 at a Marathon gas station located about 15 minutes from downtown Detroit. At least 10 cars are believed to have benefitted from the free-flowing gas pump, which still has police befuddled.

Here’s what is known about the supposed hack: Per Fox 2 Detroit, the thieves used some sort of remote device that allowed them to hijack the pump and take control away from the gas station employee. Police confirmed to the local publication that the device prevented the clerk from using the gas station’s system to shut off the individual pump.

Slashdot post.

Hard to know what’s true, but it seems like a good example of a hack against a cyber-physical system.

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Man-in-the-Middle Attack against Electronic Car-Door Openers

This is an interesting tactic, and there’s a video of it being used:

The theft took just one minute and the Mercedes car, stolen from the Elmdon area of Solihull on 24 September, has not been recovered.

In the footage, one of the men can be seen waving a box in front of the victim’s house.

The device receives a signal from the key inside and transmits it to the second box next to the car.

The car’s systems are then tricked into thinking the key is present and it unlocks, before the ignition can be started.

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