Tag Archives: cars

Hyundai Uses Example Keys for Encryption System

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/08/hyundai-uses-example-keys-for-encryption-system.html

This is a dumb crypto mistake I had not previously encountered:

A developer says it was possible to run their own software on the car infotainment hardware after discovering the vehicle’s manufacturer had secured its system using keys that were not only publicly known but had been lifted from programming examples.

[…]

“Turns out the [AES] encryption key in that script is the first AES 128-bit CBC example key listed in the NIST document SP800-38A [PDF]”.

[…]

Luck held out, in a way. “Greenluigi1” found within the firmware image the RSA public key used by the updater, and searched online for a portion of that key. The search results pointed to a common public key that shows up in online tutorials like “RSA Encryption & Decryption Example with OpenSSL in C.“

EDITED TO ADD (8/23): Slashdot post.

Surveillance of Your Car

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/08/surveillance-of-your-car.html

TheMarkup has an extensive analysis of connected vehicle data and the companies that are collecting it.

The Markup has identified 37 companies that are part of the rapidly growing connected vehicle data industry that seeks to monetize such data in an environment with few regulations governing its sale or use.

While many of these companies stress they are using aggregated or anonymized data, the unique nature of location and movement data increases the potential for violations of user privacy.

Security Vulnerabilities in Honda’s Keyless Entry System

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/07/security-vulnerabilities-in-hondas-keyless-entry-system.html

Honda vehicles from 2021 to 2022 are vulnerable to this attack:

On Thursday, a security researcher who goes by Kevin2600 published a technical report and videos on a vulnerability that he claims allows anyone armed with a simple hardware device to steal the code to unlock Honda vehicles. Kevin2600, who works for cybersecurity firm Star-V Lab, dubbed the attack RollingPWN.

[…]

In a phone call, Kevin2600 explained that the attack relies on a weakness that allows someone using a software defined radio—such as HackRF—to capture the code that the car owner uses to open the car, and then replay it so that the hacker can open the car as well. In some cases, he said, the attack can be performed from 30 meters (approximately 98 feet) away.

In the videos, Kevin2600 and his colleagues show how the attack works by unlocking different models of Honda cars with a device connected to a laptop.

The Honda models that Kevin2600 and his colleagues tested the attack on use a so-called rolling code mechanism, which means that­—in theory­—every time the car owner uses the keyfob, it sends a different code to open it. This should make it impossible to capture the code and use it again. But the researchers found that there is a flaw that allows them to roll back the codes and reuse old codes to open the car, Kevin2600 said.

Hacking Tesla’s Remote Key Cards

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/06/hacking-teslas-remote-key-cards.html

Interesting vulnerability in Tesla’s NFC key cards:

Martin Herfurt, a security researcher in Austria, quickly noticed something odd about the new feature: Not only did it allow the car to automatically start within 130 seconds of being unlocked with the NFC card, but it also put the car in a state to accept entirely new keys—with no authentication required and zero indication given by the in-car display.

“The authorization given in the 130-second interval is too general… [it’s] not only for drive,” Herfurt said in an online interview. “This timer has been introduced by Tesla…in order to make the use of the NFC card as a primary means of using the car more convenient. What should happen is that the car can be started and driven without the user having to use the key card a second time. The problem: within the 130-second period, not only the driving of the car is authorized, but also the [enrolling] of a new key.”

Forging Australian Driver’s Licenses

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/05/forging-australian-drivers.html

The New South Wales digital driver’s license has multiple implementation flaws that allow for easy forgeries.

This file is encrypted using AES-256-CBC encryption combined with Base64 encoding.

A 4-digit application PIN (which gets set during the initial onboarding when a user first instals the application) is the encryption password used to protect or encrypt the licence data.

The problem here is that an attacker who has access to the encrypted licence data (whether that be through accessing a phone backup, direct access to the device or remote compromise) could easily brute-force this 4-digit PIN by using a script that would try all 10,000 combinations….

[…]

The second design flaw that is favourable for attackers is that the Digital Driver Licence data is never validated against the back-end authority which is the Service NSW API/database.

This means that the application has no native method to validate the Digital Driver Licence data that exists on the phone and thus cannot perform further actions such as warn users when this data has been modified.

As the Digital Licence is stored on the client’s device, validation should take place to ensure the local copy of the data actually matches the Digital Driver’s Licence data that was originally downloaded from the Service NSW API.

As this verification does not take place, an attacker is able to display the edited data on the Service NSW application without any preventative factors.

There’s a lot more in the blog post.

Bluetooth Flaw Allows Remote Unlocking of Digital Locks

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/05/bluetooth-flaw-allows-remote-unlocking-of-digital-locks.html

Locks that use Bluetooth Low Energy to authenticate keys are vulnerable to remote unlocking. The research focused on Teslas, but the exploit is generalizable.

In a video shared with Reuters, NCC Group researcher Sultan Qasim Khan was able to open and then drive a Tesla using a small relay device attached to a laptop which bridged a large gap between the Tesla and the Tesla owner’s phone.

“This proves that any product relying on a trusted BLE connection is vulnerable to attacks even from the other side of the world,” the UK-based firm said in a statement, referring to the Bluetooth Low Energy (BLE) protocol—technology used in millions of cars and smart locks which automatically open when in close proximity to an authorised device.

Although Khan demonstrated the hack on a 2021 Tesla Model Y, NCC Group said any smart locks using BLE technology, including residential smart locks, could be unlocked in the same way.

Another news article.

Surveillance by Driverless Car

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/05/surveillance-by-driverless-car.html

San Francisco police are using autonomous vehicles as mobile surveillance cameras.

Privacy advocates say the revelation that police are actively using AV footage is cause for alarm.

“This is very concerning,” Electronic Frontier Foundation (EFF) senior staff attorney Adam Schwartz told Motherboard. He said cars in general are troves of personal consumer data, but autonomous vehicles will have even more of that data from capturing the details of the world around them. “So when we see any police department identify AVs as a new source of evidence, that’s very concerning.”

Apple AirTags Are Being Used to Track People and Cars

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/12/apple-airtags-are-being-used-to-track-people-and-cars.html

This development suprises no one who has been paying attention:

Researchers now believe AirTags, which are equipped with Bluetooth technology, could be revealing a more widespread problem of tech-enabled tracking. They emit a digital signal that can be detected by devices running Apple’s mobile operating system. Those devices then report where an AirTag has last been seen. Unlike similar tracking products from competitors such as Tile, Apple added features to prevent abuse, including notifications like the one Ms. Estrada received and automatic beeping. (Tile plans to release a feature to prevent the tracking of people next year, a spokeswoman for that company said.)

[…]

A person who doesn’t own an iPhone might have a harder time detecting an unwanted AirTag. AirTags aren’t compatible with Android smartphones. Earlier this month, Apple released an Android app that can scan for AirTags — but you have to be vigilant enough to download it and proactively use it.

Apple declined to say if it was working with Google on technology that would allow Android phones to automatically detect its trackers.

People who said they have been tracked have called Apple’s safeguards insufficient. Ms. Estrada said she was notified four hours after her phone first noticed the rogue gadget. Others said it took days before they were made aware of an unknown AirTag. According to Apple, the timing of the alerts can vary depending on the iPhone’s operating system and location settings.

Thieves Using AirTags to “Follow” Cars

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2021/12/thieves-using-airtags-to-follow-cars.html

From Ontario and not surprising:

Since September 2021, officers have investigated five incidents where suspects have placed small tracking devices on high-end vehicles so they can later locate and steal them. Brand name “air tags” are placed in out-of-sight areas of the target vehicles when they are parked in public places like malls or parking lots. Thieves then track the targeted vehicles to the victim’s residence, where they are stolen from the driveway.

Thieves typically use tools like screwdrivers to enter the vehicles through the driver or passenger door, while ensuring not to set off alarms. Once inside, an electronic device, typically used by mechanics to reprogram the factory setting, is connected to the onboard diagnostics port below the dashboard and programs the vehicle to accept a key the thieves have brought with them. Once the new key is programmed, the vehicle will start and the thieves drive it away.

I’m not sure if there’s anything that can be done:

When Apple first released AirTags earlier this year, concerns immediately sprung up about nefarious use cases for the covert trackers. Apple responded with a slew of anti-stalking measures, but those are more intended for keeping people safe than cars. An AirTag away from its owner will sound an alarm, letting anyone nearby know that it’s been left behind, but it can take up to 24 hours for that alarm to go off — more than enough time to nab a car in the dead of night.

Split-Second Phantom Images Fool Autopilots

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/10/split-second-phantom-images-fool-autopilots.html

Researchers are tricking autopilots by inserting split-second images into roadside billboards.

Researchers at Israel’s Ben Gurion University of the Negev … previously revealed that they could use split-second light projections on roads to successfully trick Tesla’s driver-assistance systems into automatically stopping without warning when its camera sees spoofed images of road signs or pedestrians. In new research, they’ve found they can pull off the same trick with just a few frames of a road sign injected on a billboard’s video. And they warn that if hackers hijacked an internet-connected billboard to carry out the trick, it could be used to cause traffic jams or even road accidents while leaving little evidence behind.

[…]

In this latest set of experiments, the researchers injected frames of a phantom stop sign on digital billboards, simulating what they describe as a scenario in which someone hacked into a roadside billboard to alter its video. They also upgraded to Tesla’s most recent version of Autopilot known as HW3. They found that they could again trick a Tesla or cause the same Mobileye device to give the driver mistaken alerts with just a few frames of altered video.

The researchers found that an image that appeared for 0.42 seconds would reliably trick the Tesla, while one that appeared for just an eighth of a second would fool the Mobileye device. They also experimented with finding spots in a video frame that would attract the least notice from a human eye, going so far as to develop their own algorithm for identifying key blocks of pixels in an image so that a half-second phantom road sign could be slipped into the “uninteresting” portions.

The paper:

Abstract: In this paper, we investigate “split-second phantom attacks,” a scientific gap that causes two commercial advanced driver-assistance systems (ADASs), Telsa Model X (HW 2.5 and HW 3) and Mobileye 630, to treat a depthless object that appears for a few milliseconds as a real obstacle/object. We discuss the challenge that split-second phantom attacks create for ADASs. We demonstrate how attackers can apply split-second phantom attacks remotely by embedding phantom road signs into an advertisement presented on a digital billboard which causes Tesla’s autopilot to suddenly stop the car in the middle of a road and Mobileye 630 to issue false notifications. We also demonstrate how attackers can use a projector in order to cause Tesla’s autopilot to apply the brakes in response to a phantom of a pedestrian that was projected on the road and Mobileye 630 to issue false notifications in response to a projected road sign. To counter this threat, we propose a countermeasure which can determine whether a detected object is a phantom or real using just the camera sensor. The countermeasure (GhostBusters) uses a “committee of experts” approach and combines the results obtained from four lightweight deep convolutional neural networks that assess the authenticity of an object based on the object’s light, context, surface, and depth. We demonstrate our countermeasure’s effectiveness (it obtains a TPR of 0.994 with an FPR of zero) and test its robustness to adversarial machine learning attacks.