Category Archives: Side-channel attacks

Leaking Screen Information on Zoom Calls through Reflections in Eyeglasses

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Okay, it’s an obscure threat. But people are researching it:

Our models and experimental results in a controlled lab setting show it is possible to reconstruct and recognize with over 75 percent accuracy on-screen texts that have heights as small as 10 mm with a 720p webcam.” That corresponds to 28 pt, a font size commonly used for headings and small headlines.

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Being able to read reflected headline-size text isn’t quite the privacy and security problem of being able to read smaller 9 to 12 pt fonts. But this technique is expected to provide access to smaller font sizes as high-resolution webcams become more common…

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New Browser De-anonymization Technique

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Researchers have a new way to de-anonymize browser users, by correlating their behavior on one account with their behavior on another:

The findings, which NJIT researchers will present at the Usenix Security Symposium in Boston next month, show how an attacker who tricks someone into loading a malicious website can determine whether that visitor controls a particular public identifier, like an email address or social media account, thus linking the visitor to a piece of potentially personal data.

When you visit a website, the page can capture your IP address, but this doesn’t necessarily give the site owner enough information to individually identify you. Instead, the hack analyzes subtle features of a potential target’s browser activity to determine whether they are logged into an account for an array of services, from YouTube and Dropbox to Twitter, Facebook, TikTok, and more. Plus the attacks work against every major browser, including the anonymity-focused Tor Browser…

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Hertzbleed: A New Side-Channel Attack

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Hertzbleed is a new side-channel attack that works against a variety of microprocressors. Deducing cryptographic keys by analyzing power consumption has long been an attack, but it’s not generally viable because measuring power consumption is often hard. This new attack measures power consumption by measuring time, making it easier to exploit.

The team discovered that dynamic voltage and frequency scaling (DVFS)—a power and thermal management feature added to every modern CPU—allows attackers to deduce the changes in power consumption by monitoring the time it takes for a server to respond to specific carefully made queries. The discovery greatly reduces what’s required. With an understanding of how the DVFS feature works, power side-channel attacks become much simpler timing attacks that can be done remotely…

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Remotely Controlling Touchscreens

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Researchers have demonstrated controlling touchscreens at a distance, at least in a laboratory setting:

The core idea is to take advantage of the electromagnetic signals to execute basic touch events such as taps and swipes into targeted locations of the touchscreen with the goal of taking over remote control and manipulating the underlying device.

The attack, which works from a distance of up to 40mm, hinges on the fact that capacitive touchscreens are sensitive to EMI, leveraging it to inject electromagnetic signals into transparent electrodes that are built into the touchscreen so as to register them as touch events…

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New Rowhammer Technique

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Rowhammer is an attack technique involving accessing — that’s “hammering” — rows of bits in memory, millions of times per second, with the intent of causing bits in neighboring rows to flip. This is a side-channel attack, and the result can be all sorts of mayhem.

Well, there is a new enhancement:

All previous Rowhammer attacks have hammered rows with uniform patterns, such as single-sided, double-sided, or n-sided. In all three cases, these “aggressor” rows — meaning those that cause bitflips in nearby “victim” rows — are accessed the same number of times. …

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“Alexa, Stop Listening to Me or I’ll Cut Your Ears Off”

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Since we’ve started inviting them into our homes, many of us have began casting a wary eye at our smart speakers. What exactly are they doing with the constant stream of audio we generate, some of it coming from the …read more

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Cloning Google Titan 2FA keys

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This is a clever side-channel attack:

The cloning works by using a hot air gun and a scalpel to remove the plastic key casing and expose the NXP A700X chip, which acts as a secure element that stores the cryptographic secrets. Next, an attacker connec… Continue reading Cloning Google Titan 2FA keys→

Eavesdropping on Phone Taps from Voice Assistants

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The microphones on voice assistants are very sensitive, and can snoop on all sorts of data:

In Hey Alexa what did I just type? we show that when sitting up to half a meter away, a voice assistant can still hear the taps you make on your phone, even in… Continue reading Eavesdropping on Phone Taps from Voice Assistants→

Manipulating Systems Using Remote Lasers

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Many systems are vulnerable:

Researchers at the time said that they were able to launch inaudible commands by shining lasers — from as far as 360 feet — at the microphones on various popular voice assistants, including Amazon Alexa, Apple Siri, Faceb… Continue reading Manipulating Systems Using Remote Lasers→

Determining What Video Conference Participants Are Typing from Watching Shoulder Movements

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Accuracy isn’t great, but that it can be done at all is impressive.

Murtuza Jadiwala, a computer science professor heading the research project, said his team was able to identify the contents of texts by examining body movement of the participants. Specifically, they focused on the movement of their shoulders and arms to extrapolate the actions of their fingers as they typed.

Given the widespread use of high-resolution web cams during conference calls, Jadiwala was able to record and analyze slight pixel shifts around users’ shoulders to determine if they were moving left or right, forward or backward. He then created a software program that linked the movements to a list of commonly used words. He says the “text inference framework that uses the keystrokes detected from the video … predict[s] words that were most likely typed by the target user. We then comprehensively evaluate[d] both the keystroke/typing detection and text inference frameworks using data collected from a large number of participants.”…

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