SSL and internet security news

cars

Auto Added by WPeMatico

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.

Powered by WPeMatico

Unfixable Automobile Computer Security Vulnerability

There is an unpatchable vulnerability that affects most modern cars. It’s buried in the Controller Area Network (CAN):

Researchers say this flaw is not a vulnerability in the classic meaning of the word. This is because the flaw is more of a CAN standard design choice that makes it unpatchable.

Patching the issue means changing how the CAN standard works at its lowest levels. Researchers say car manufacturers can only mitigate the vulnerability via specific network countermeasures, but cannot eliminate it entirely.

Details on how the attack works are here:

The CAN messages, including errors, are called “frames.” Our attack focuses on how CAN handles errors. Errors arise when a device reads values that do not correspond to the original expected value on a frame. When a device detects such an event, it writes an error message onto the CAN bus in order to “recall” the errant frame and notify the other devices to entirely ignore the recalled frame. This mishap is very common and is usually due to natural causes, a transient malfunction, or simply by too many systems and modules trying to send frames through the CAN at the same time.

If a device sends out too many errors, then­ — as CAN standards dictate — ­it goes into a so-called Bus Off state, where it is cut off from the CAN and prevented from reading and/or writing any data onto the CAN. This feature is helpful in isolating clearly malfunctioning devices and stops them from triggering the other modules/systems on the CAN.

This is the exact feature that our attack abuses. Our attack triggers this particular feature by inducing enough errors such that a targeted device or system on the CAN is made to go into the Bus Off state, and thus rendered inert/inoperable. This, in turn, can drastically affect the car’s performance to the point that it becomes dangerous and even fatal, especially when essential systems like the airbag system or the antilock braking system are deactivated. All it takes is a specially-crafted attack device, introduced to the car’s CAN through local access, and the reuse of frames already circulating in the CAN rather than injecting new ones (as previous attacks in this manner have done).

Slashdot thread.

Powered by WPeMatico

Confusing Self-Driving Cars by Altering Road Signs

Researchers found that they could confuse the road sign detection algorithms of self-driving cars by adding stickers to the signs on the road. They could, for example, cause a car to think that a stop sign is a 45 mph speed limit sign. The changes are subtle, though — look at the photo from the article.

Research paper:

Robust Physical-World Attacks on Machine Learning Models,” by Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, and Dawn Song:

Abstract: Deep neural network-based classifiers are known to be vulnerable to adversarial examples that can fool them into misclassifying their input through the addition of small-magnitude perturbations. However, recent studies have demonstrated that such adversarial examples are not very effective in the physical world–they either completely fail to cause misclassification or only work in restricted cases where a relatively complex image is perturbed and printed on paper. In this paper we propose a new attack algorithm–Robust Physical Perturbations (RP2)– that generates perturbations by taking images under different conditions into account. Our algorithm can create spatially-constrained perturbations that mimic vandalism or art to reduce the likelihood of detection by a casual observer. We show that adversarial examples generated by RP2 achieve high success rates under various conditions for real road sign recognition by using an evaluation methodology that captures physical world conditions. We physically realized and evaluated two attacks, one that causes a Stop sign to be misclassified as a Speed Limit sign in 100% of the testing conditions, and one that causes a Right Turn sign to be misclassified as either a Stop or Added Lane sign in 100% of the testing conditions.

Powered by WPeMatico

Uber Drivers Hacking the System to Cause Surge Pricing

Interesting story about Uber drivers who have figured out how to game the company’s algorithms to cause surge pricing:

According to the study. drivers manipulate Uber’s algorithm by logging out of the app at the same time, making it think that there is a shortage of cars.

[…]

The study said drivers have been coordinating forced surge pricing, after interviews with drivers in London and New York, and research on online forums such as Uberpeople.net. In a post on the website for drivers, seen by the researchers, one person said: “Guys, stay logged off until surge. Less supply high demand = surge.”

.

Passengers, of course, have long had tricks to avoid surge pricing.

I expect to see more of this sort of thing as algorithms become more prominent in our lives.

Powered by WPeMatico

Vulnerabilities in Car Washes

Articles about serious vulnerabilities in IoT devices and embedded systems are now dime-a-dozen. This one concerns Internet-connected car washes:

A group of security researchers have found vulnerabilities in internet-connected drive-through car washes that would let hackers remotely hijack the systems to physically attack vehicles and their occupants. The vulnerabilities would let an attacker open and close the bay doors on a car wash to trap vehicles inside the chamber, or strike them with the doors, damaging them and possibly injuring occupants.

Powered by WPeMatico

Dubai Deploying Autonomous Robotic Police Cars

It’s hard to tell how much of this story is real and how much is aspirational, but it really is only a matter of time:

About the size of a child’s electric toy car, the driverless vehicles will patrol different areas of the city to boost security and hunt for unusual activity, all the while scanning crowds for potential persons of interest to police and known criminals.

Powered by WPeMatico

How the Media Influences Our Fear of Terrorism

Good article that crunches the data and shows that the press’s coverage of terrorism is disproportional to its comparative risk.

This isn’t new. I’ve written about it before, and wrote about it more generally when I wrote about the psychology of risk, fear, and security. Basically, the issue is the availability heuristic. We tend to infer the probability of something by how easy it is to bring examples of the thing to mind. So if we can think of a lot of tiger attacks in our community, we infer that the risk is high. If we can’t think of many lion attacks, we infer that the risk is low. But while this is a perfectly reasonable heuristic when living in small family groups in the East African highlands in 100,000 BC, it fails in the face of modern media. The media makes the rare seem more common by spending a lot of time talking about it. It’s not the media’s fault. By definition, news is “something that hardly ever happens.” But when the coverage of terrorist deaths exceeds the coverage of homicides, we have a tendency to mistakenly inflate the risk of the former while discount the risk of the latter.

Our brains aren’t very good at probability and risk analysis. We tend to exaggerate spectacular, strange and rare events, and downplay ordinary, familiar and common ones. We think rare risks are more common than they are. We fear them more than probability indicates we should.

There is a lot of psychological research that tries to explain this, but one of the key findings is this: People tend to base risk analysis more on stories than on data. Stories engage us at a much more visceral level, especially stories that are vivid, exciting or personally involving.

If a friend tells you about getting mugged in a foreign country, that story is more likely to affect how safe you feel traveling to that country than reading a page of abstract crime statistics will.

Novelty plus dread plus a good story equals overreaction.

It’s not just murders. It’s flying vs. driving: the former is much safer, but the latter is more spectacular when it occurs.

Powered by WPeMatico

Hacking Wireless Tire-Pressure Monitoring System

Research paper: “Security and Privacy Vulnerabilities of In-Car Wireless Networks: A Tire Pressure Monitoring System Case Study,” by Ishtiaq Rouf, Rob Miller, Hossen Mustafa, Travis Taylor, Sangho Oh, Wenyuan Xu, Marco Gruteser, Wade Trapper, Ivan Seskar:

Abstract: Wireless networks are being integrated into the modern automobile. The security and privacy implications of such in-car networks, however, have are not well understood as their transmissions propagate beyond the confines of a car’s body. To understand the risks associated with these wireless systems, this paper presents a privacy and security evaluation of wireless Tire Pressure Monitoring Systems using both laboratory experiments with isolated tire pressure sensor modules and experiments with a complete vehicle system. We show that eavesdropping is easily possible at a distance of roughly 40m from a passing vehicle. Further, reverse-engineering of the underlying protocols revealed static 32 bit identifiers and that messages can be easily triggered remotely, which raises privacy concerns as vehicles can be tracked through these identifiers. Further, current protocols do not employ authentication and vehicle implementations do not perform basic input validation, thereby allowing for remote spoofing of sensor messages. We validated this experimentally by triggering tire pressure warning messages in a moving vehicle from a customized software radio attack platform located in a nearby vehicle. Finally, the paper concludes with a set of recommendations for improving the privacy and security of tire pressure monitoring systems and other forthcoming in-car wireless sensor networks.

Powered by WPeMatico