Entries Tagged "cars"

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Attacking Driverless Cars with Projected Images

Interesting research—”Phantom Attacks Against Advanced Driving Assistance Systems“:

Abstract: The absence of deployed vehicular communication systems, which prevents the advanced driving assistance systems (ADASs) and autopilots of semi/fully autonomous cars to validate their virtual perception regarding the physical environment surrounding the car with a third party, has been exploited in various attacks suggested by researchers. Since the application of these attacks comes with a cost (exposure of the attacker’s identity), the delicate exposure vs. application balance has held, and attacks of this kind have not yet been encountered in the wild. In this paper, we investigate a new perceptual challenge that causes the ADASs and autopilots of semi/fully autonomous to consider depthless objects (phantoms) as real. We show how attackers can exploit this perceptual challenge to apply phantom attacks and change the abovementioned balance, without the need to physically approach the attack scene, by projecting a phantom via a drone equipped with a portable projector or by presenting a phantom on a hacked digital billboard that faces the Internet and is located near roads. We show that the car industry has not considered this type of attack by demonstrating the attack on today’s most advanced ADAS and autopilot technologies: Mobileye 630 PRO and the Tesla Model X, HW 2.5; our experiments show that when presented with various phantoms, a car’s ADAS or autopilot considers the phantoms as real objects, causing these systems to trigger the brakes, steer into the lane of oncoming traffic, and issue notifications about fake road signs. In order to mitigate this attack, we present a model that analyzes a detected object’s context, surface, and reflected light, which is capable of detecting phantoms with 0.99 AUC. Finally, we explain why the deployment of vehicular communication systems might reduce attackers’ opportunities to apply phantom attacks but won’t eliminate them.

The paper will be presented at CyberTech at the end of the month.

Posted on February 3, 2020 at 6:24 AMView Comments

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.

Posted on November 13, 2019 at 6:16 AMView Comments

Detecting Credit Card Skimmers

Modern credit card skimmers hidden in self-service gas pumps communicate via Bluetooth. There’s now an app that can detect them:

The team from the University of California San Diego, who worked with other computer scientists from the University of Illinois, developed an app called Bluetana which not only scans and detects Bluetooth signals, but can actually differentiate those coming from legitimate devices—like sensors, smartphones, or vehicle tracking hardware—from card skimmers that are using the wireless protocol as a way to harvest stolen data. The full details of what criteria Bluetana uses to differentiate the two isn’t being made public, but its algorithm takes into account metrics like signal strength and other telltale markers that were pulled from data based on scans made at 1,185 gas stations across six different states.

Posted on August 26, 2019 at 6:41 AMView Comments

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.”

Posted on August 23, 2019 at 6:19 AMView Comments

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.

Posted on August 22, 2019 at 5:21 AMView Comments

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.

Posted on April 4, 2019 at 6:18 AMView Comments

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.

Posted on March 20, 2019 at 12:38 PMView Comments

Sidebar photo of Bruce Schneier by Joe MacInnis.