Due to the way CMOS cameras operate, rapidly changing light from fast flashing diodes can be used to vary the color. For example, the shade of red on a stop sign could look different on each line depending on the time between the diode flash and the line capture.

The result is the camera capturing an image full of lines that don’t quite match each other. The information is cropped and sent to the classifier, usually based on deep neural networks, for interpretation. Because it’s full of lines that don’t match, the classifier doesn’t recognize the image as a traffic sign...

The threat resides in the chips’ data memory-dependent prefetcher, a hardware optimization that predicts the memory addresses of data that running code is likely to access in the near future. By loading the contents into the CPU cache before it’s actually needed, the DMP, as the feature is abbreviated, reduces latency between the main memory and the CPU, a common bottleneck in modern computing. DMPs are a relatively new phenomenon found only in M-series chips and Intel’s 13th-generation Raptor Lake microarchitecture, although older forms of prefetchers have been common for years...

Abstract:

We show that built-in sensors in commodity PCs, such as microphones, inadvertently capture electromagnetic side-channel leakage from ongoing computation. Moreover, this information is often conveyed by supposedly-benign channels such as audio recordings and common Voice-over-IP applications, even after lossy compression.

Thus, we show, it is possible to conduct physical side-channel attacks on computation by remote and purely passive analysis of commonly-shared channels. These attacks require neither physical proximity (which could be mitigated by distance and shielding), nor the ability to run code on the target or configure its hardware. Consequently, we argue, physical side channels on PCs can no longer be excluded from remote-attack threat models...

“A Practical Deep Learning-Based Acoustic Side Channel Attack on Keyboards”

Abstract: With recent developments in deep learning, the ubiquity of microphones and the rise in online services via personal devices, acoustic side channel attacks present a greater threat to keyboards than ever. This paper presents a practical implementation of a state-of-the-art deep learning model in order to classify laptop keystrokes, using a smartphone integrated microphone. When trained on keystrokes recorded by a nearby phone, the classifier achieved an accuracy of 95%, the highest accuracy seen without the use of a language model. When trained on keystrokes recorded using the video-conferencing software Zoom, an accuracy of 93% was achieved, a new best for the medium. Our results prove the practicality of these side channel attacks via off-the-shelf equipment and algorithms. We discuss a series of mitigation methods to protect users against these series of attacks...

The first attack uses an Internet-connected surveillance camera to take a high-speed video of the power LED on a smart card reader­—or of an attached peripheral device—­during cryptographic operations. This technique allowed the researchers to pull a 256-bit ECDSA key off the same government-approved smart card used in Minerva. The other allowed the researchers to recover the private SIKE key of a Samsung Galaxy S8 phone by training the camera of an iPhone 13 on the power LED of a USB speaker connected to the handset, in a similar way to how Hertzbleed pulled SIKE keys off Intel and AMD CPUs...

Researchers have just published a side-channel attack—using power consumption—against an implementation of the algorithm that was supposed to be resistant against that sort of attack.

The algorithm is not “broken” or “cracked”—despite headlines to the contrary—this is just a side-channel attack. What makes this work really interesting is that the researchers used a machine-learning model to train the system to exploit the side channel...

We provide an extensive cryptographic analysis of Threema, a Swiss-based encrypted messaging application with more than 10 million users and 7000 corporate customers. We present seven different attacks against the protocol in three different threat models. As one example, we present a cross-protocol attack which breaks authentication in Threema and which exploits the lack of proper key separation between different sub-protocols. As another, we demonstrate a compression-based side-channel attack that recovers users’ long-term private keys through observation of the size of Threema encrypted back-ups. We discuss remediations for our attacks and draw three wider lessons for developers of secure protocols...

Abstract: Eavesdropping from the user’s smartphone is a well-known threat to the user’s safety and privacy. Existing studies show that loudspeaker reverberation can inject speech into motion sensor readings, leading to speech eavesdropping. While more devastating attacks on ear speakers, which produce much smaller scale vibrations, were believed impossible to eavesdrop with zero-permission motion sensors. In this work, we revisit this important line of reach. We explore recent trends in smartphone manufacturers that include extra/powerful speakers in place of small ear speakers, and demonstrate the feasibility of using motion sensors to capture such tiny speech vibrations. We investigate the impacts of these new ear speakers on built-in motion sensors and examine the potential to elicit private speech information from the minute vibrations. Our designed system ...

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.

[…]

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...

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...

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...

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...

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. ...

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 connects the chip to hardware and software that take measurements as the key is being used to authenticate on an existing account. Once the measurement-taking is finished, the attacker seals the chip in a new casing and returns it to the victim.

Extracting and later resealing the chip takes about four hours. It takes another six hours to take measurements for each account the attacker wants to hack. In other words, the process would take 10 hours to clone the key for a single account, 16 hours to clone a key for two accounts, and 22 hours for three accounts...

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 presence of noise. Modern voice assistants have two to seven microphones, so they can do directional localisation, just as human ears do, but with greater sensitivity. We assess the risk and show that a lot more work is needed to understand the privacy implications of the always-on microphones that are increasingly infesting our work spaces and our homes...

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, Facebook Portal, and Google Assistant.

[…]

They broadened their research to show how light can be used to manipulate a wider range of digital assistants — including Amazon Echo 3 — but also sensing systems found in medical devices, autonomous vehicles, industrial systems and even space systems.

The researchers also delved into how the ecosystem of devices connected to voice-activated assistants — such as smart-locks, home switches and even cars — also fail under common security vulnerabilities that can make these attacks even more dangerous. The paper shows how using a digital assistant as the gateway can allow attackers to take control of other devices in the home: Once an attacker takes control of a digital assistant, he or she can have the run of any device connected to it that also responds to voice commands. Indeed, these attacks can get even more interesting if these devices are connected to other aspects of the smart home, such as smart door locks, garage doors, computers and even people’s cars, they said...

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.”...