Dark Reading is part of the Informa Tech Division of Informa PLC

This site is operated by a business or businesses owned by Informa PLC and all copyright resides with them.Informa PLC's registered office is 5 Howick Place, London SW1P 1WG. Registered in England and Wales. Number 8860726.

IoT/Embedded Security

7/17/2018
08:05 AM
Larry Loeb
Larry Loeb
Larry Loeb
50%
50%

Researchers Detail Spoofing Attack Against Vehicle GPS

A new paper shows that with the right amount of hardware and know-how, an attacker can spoof a vehicle's GPS system and change the route.

Researchers from Virginia Tech, the University of Electronic Science and Technology of China and Microsoft Research have come up with a way to mess with the GPS directional systems in a car. The type of spoofing attack means that a false route will supplant the one derived by the car's navigation system.

The attack is stealthy, and the driver will not realize that the attack is underway.

In their paper, "All Your GPS Are Belong To Us: Towards Stealthy Manipulation of Road Navigation Systems," the researchers outline how they did it.

The goal for the researchers was to manipulate the turn-by-turn navigation and guide a victim to a wrong destination without being noticed. Most navigation systems display the "first-person" view which forces users to focus on the current road and the next turn.

However, the researchers found that if an attacker identifies an attacking route that mimics the shape of the route displayed on the map, it is possible to trigger navigation instructions that are consistent with the physical environment -- like triggering the "turning right" prompt only when there is an actual right-turn ahead -- to avoid alerting users.

The research did show that hardware is needed.

They came up with a $223 portable spoofer -- made up of a HackRF One-based frontend, a Raspberry Pi, a portable power source and an antenna -- that could penetrate the car body to take control of the GPS navigation system.

The research found that the effective spoofing range is 40 to 50 meters and the target device can consistently latch onto the false signals without lost connections.

They also came up with an algorithm the crafts the GPS inputs to the target device such that the triggered navigation instruction and displayed routes on the map remain consistent with the physical road network. It was evaluated using real-world taxi driving traces from New York City and Boston.

The group also found that the attack works for small cities, but yields fewer options for an attacker.

There are limitations in how this attack works.

To ensure a stealthy attack, researchers had to compromise on other factors.

For example, the effectiveness of the attack would be decreased in suburb or rural area with sparse road structures. Also, even the researchers say that the attack does not work on all users. They think that the general population would be more susceptible compared to tech-savvy users.


Boost your understanding of new cybersecurity approaches at Light Reading's Automating Seamless Security event on October 17 in Chicago! Service providers and enterprise receive FREE passes. All others can save 20% off passes using the code LR20 today!

The research was also limited by its structure. The study was only tested on one route, and the route did not contain wrong-ways or loops. In practice, once users enter the wrong way, they may recognize the attack.

In addition, since the attackers had to analyze the area the user was going to enter beforehand, this was not a real-time attack that updated itself on the fly. It would take some massive changes in how this attack is done to be real time, but this version does show how real-time attacks could be possible.

The attack described in the paper shows the weaknesses inherent in a GPS-based navigation system, however. It points out what changes are needed, including upgrading civilian GPS systems, trusted ground infrastructures and computer vision techniques to automatically cross-examine physical-world landmarks, and the like, to assure future attacks can be mitigated.

Related posts:

— Larry Loeb has written for many of the last century's major "dead tree" computer magazines, having been, among other things, a consulting editor for BYTE magazine and senior editor for the launch of WebWeek.

Comment  | 
Print  | 
More Insights
Comments
Newest First  |  Oldest First  |  Threaded View
COVID-19: Latest Security News & Commentary
Dark Reading Staff 9/25/2020
Hacking Yourself: Marie Moe and Pacemaker Security
Gary McGraw Ph.D., Co-founder Berryville Institute of Machine Learning,  9/21/2020
Startup Aims to Map and Track All the IT and Security Things
Kelly Jackson Higgins, Executive Editor at Dark Reading,  9/22/2020
Register for Dark Reading Newsletters
White Papers
Video
Cartoon
Current Issue
Special Report: Computing's New Normal
This special report examines how IT security organizations have adapted to the "new normal" of computing and what the long-term effects will be. Read it and get a unique set of perspectives on issues ranging from new threats & vulnerabilities as a result of remote working to how enterprise security strategy will be affected long term.
Flash Poll
How IT Security Organizations are Attacking the Cybersecurity Problem
How IT Security Organizations are Attacking the Cybersecurity Problem
The COVID-19 pandemic turned the world -- and enterprise computing -- on end. Here's a look at how cybersecurity teams are retrenching their defense strategies, rebuilding their teams, and selecting new technologies to stop the oncoming rise of online attacks.
Twitter Feed
Dark Reading - Bug Report
Bug Report
Enterprise Vulnerabilities
From DHS/US-CERT's National Vulnerability Database
CVE-2020-15208
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, when determining the common dimension size of two tensors, TFLite uses a `DCHECK` which is no-op outside of debug compilation modes. Since the function always returns the dimension of the first tensor, malicious attackers can ...
CVE-2020-15209
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a `nullptr` buffer. This can be achieved by changing a buffer index in the flatbuffer serialization to convert a read-only tensor to a read-write one....
CVE-2020-15210
PUBLISHED: 2020-09-25
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and ...
CVE-2020-15211
PUBLISHED: 2020-09-25
In TensorFlow Lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, saved models in the flatbuffer format use a double indexing scheme: a model has a set of subgraphs, each subgraph has a set of operators and each operator has a set of input/output tensors. The flatbuffer format uses indices f...
CVE-2020-15212
PUBLISHED: 2020-09-25
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `outpu...