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Researchers have used thermal cameras and ML guessing techniques to recover passwords from measuring the residual heat left by fingers on keyboards. From the abstract:

We detail the implementation of ThermoSecure and make a dataset of 1,500 thermal images of keyboards with heat traces resulting from input publicly available. Our first study shows that ThermoSecure successfully attacks 6-symbol, 8-symbol, 12-symbol, and 16-symbol passwords with an average accuracy of 92%, 80%, 71%, and 55% respectively, and even higher accuracy when thermal images are taken within 30 seconds. We found that typing behavior significantly impacts vulnerability to thermal attacks, where hunt-and-peck typists are more vulnerable than fast typists (92% vs 83% thermal attack success if performed within 30 seconds). The second study showed that the keycaps material has a statistically significant effect on the effectiveness of thermal attacks: ABS keycaps retain the thermal trace of users presses for a longer period of time, making them more vulnerable to thermal attacks, with a 52% average attack accuracy compared to 14% for keyboards with PBT keycaps.

“ABS” is Acrylonitrile Butadiene Styrene, which some keys are made of. Others are made of Polybutylene Terephthalate (PBT). PBT keys are less vulnerable.

But, honestly, if someone can train a camera at your keyboard, you have bigger problems.

News article.

“I’ll just Uber home.” 

Who hails a taxi anymore? These days, city streets are full of double-parked sedans with their hazards on, looking for their charges. Uber is synonymous with ridesharing and has made it so far into our culture that it’s not just a company name but a verb.  

Uber’s reputation has ebbed and flowed since its creation in 2009, and it’s taken another hit recently as more details are coming to light about a massive 2016 cybersecurity breach and the chief security officer’s attempts to cover it up.  

What Happened in the 2016 Uber Breach?

In 2016, a ransomware group trawled the internet and gathered Uber’s credentials that opened the door into the company’s server database. The cybercriminals then stole the information of customers and drivers alike and held it for a $100,000 Bitcoin ransom. Joe Sullivan, Uber’s chief security officer at the time, paid the ransom and the criminal group agreed to delete the information they uncovered. While it’s not uncommon for large corporations to give in to cybercriminals and dole out huge ransom payments, Sullivan is facing potential jail time because he didn’t report the incident to the Federal Trade Commission. He was recently found guilty of wire fraud and concealing a felony from authorities.  

Uber account holders had their personally identifiable information in nefarious hands without their knowledge. The cybercriminals allegedly downloaded the names, email addresses and phone numbers of 57 million Uber customers and drivers, plus the license plate numbers of 600,000 drivers.1  

Why It’s Important for Companies to Report Leaks

Organizations have a responsibility to their customers to report any cyberbreaches. With a full name, email address, and phone number, cybercriminals can inflict a lot of damage on an innocent person’s credit, steal money from online accounts, or invade someone’s digital privacy. Customers must act swiftly to put the proper safeguards in place, but they can’t do that if they don’t even know a breach has happened! The longer a cybercriminal has to poke and prod someone’s digital footprint, the more havoc they can wreak and profits they can gain. 

How to Protect Your Personal Information Before and After a Breach 

Acting swiftly is key to keeping your personally identifiable information (PII) private after a breach, though there are a few measures you can take right now that could prevent your information from being compromised. Here’s what you can do before and after a breach. 

Preventive measures

One way to shrink your attack surface – or the number of possible entry points into your digital life – is to regularly vet your online accounts and apps. For example, when you’re cleaning your closet, it’s common to donate or trash any clothing you haven’t worn in a year. The same method works for your digital life. If you haven’t logged into a shopping site or mobile gaming app in over a year, it’s unlikely that you will use them anytime soon, so it’s time to say goodbye and delete it. 

McAfee credit lock and security freeze are other preventive measures that can keep your credit safe in case your PII is ever compromised. These services make it easy to prevent one or all three major credit bureaus from accessing your credit. In turn, this prevents anyone other than you from opening a bank account, applying for a loan, or making a substantial purchase. If you’re not planning on needing a credit report, it’s a great practice to freeze your credit. 

Reactive measures

When you first hear of a company’s data leak with which you have an account, the first step you should take is to change your account password. Login and password combinations are often compromised in a data breach. Make sure your new password is strong and is not a duplicate of a password you use elsewhere. 

Next, consider running a Personal Data Cleanup scan. Personal Data Cleanup checks risky data broker sites and alerts you if your information appears on any of them. From there, you can take steps to remove your information. 

Finally, for the next few weeks, keep close tabs on your financial, online, and email accounts. Watch for suspicious activities like purchases you didn’t make, electronic receipts, notifications, or mailing lists that you didn’t sign up for. McAfee+ Ultimate can help you here with its identity monitoring and full-service Personal Data Cleanup. McAfee+ gives you a partner to alert you and help you recover if your digital privacy is compromised. 

Constant Vigilance and Digital Confidence-Boosting Assets

Protecting your identity and digital privacy is a two-way street. While identity and privacy protection tools go a long way, individuals also have a responsibility to remain vigilant and take quick action if they suspect their information is compromised. And the ultimate responsibility lies with companies to alert the authorities and their customers after a data leak and to take serious steps to shore up their security to make sure it never happens again. 

1The Verge, “Former Uber security chief found guilty of covering up massive 2016 data breach” 

The post 57 Million Users Compromised in Uber Leak: Protect Your Digital Privacy and Identity appeared first on McAfee Blog.

Trend Micro found evidence of new PayPal scammers impersonating crypto-related businesses

The leak was caused by an access key being made publicly available on GitHub for almost five years

Google embeds in cloud security market with new software suite

Hacking on actual computers is one thing, but hacking purposefully on imaginary computers is, these days, something we can only imagine.

The websites of several major US airports were disrupted on October 10, 2022

Many admitted to sending messages on Teams they should not have

The security risks posed by deepfake technology are increasing

Interesting research: “ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks, by Tim Clifford, Ilia Shumailov, Yiren Zhao, Ross Anderson, and Robert Mullins:

Abstract: Early backdoor attacks against machine learning set off an arms race in attack and defence development. Defences have since appeared demonstrating some ability to detect backdoors in models or even remove them. These defences work by inspecting the training data, the model, or the integrity of the training procedure. In this work, we show that backdoors can be added during compilation, circumventing any safeguards in the data preparation and model training stages. As an illustration, the attacker can insert weight-based backdoors during the hardware compilation step that will not be detected by any training or data-preparation process. Next, we demonstrate that some backdoors, such as ImpNet, can only be reliably detected at the stage where they are inserted and removing them anywhere else presents a significant challenge. We conclude that machine-learning model security requires assurance of provenance along the entire technical pipeline, including the data, model architecture, compiler, and hardware specification.

Ross Anderson explains the significance:

The trick is for the compiler to recognise what sort of model it’s compiling—whether it’s processing images or text, for example—and then devising trigger mechanisms for such models that are sufficiently covert and general. The takeaway message is that for a machine-learning model to be trustworthy, you need to assure the provenance of the whole chain: the model itself, the software tools used to compile it, the training data, the order in which the data are batched and presented—in short, everything.