Collaborate Disseminate
There is a side-channel attack against YubiKey access tokens that allows someone to clone a device. It’s a complicated attack, requiring the victim’s username and password, and physical access to their YubiKey—as well as some technica… Continue reading YubiKey Side-Channel Attack
This is another attack that convinces the AI to ignore road signs:
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…
It’s yet another hardware side-channel attack:
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…
Continue reading Hardware Vulnerability in Apple’s M-Series Chips
Really interesting research: “Lend Me Your Ear: Passive Remote Physical Side Channels on PCs.”
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…
Espressif’s ESP32 microcontrollers come with a Flash encryption feature that when enabled ensures that the data and code stored on the (usually external) Flash chip is encrypted with AES-256 (ESP32) …read more Continue reading Breaking The Flash Encryption Feature Of Espressif’s Microcontrollers
Researchers have trained a ML model to detect keystrokes by sound with 95% accuracy.
“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…
Continue reading Using Machine Learning to Detect Keystrokes
This is a clever new side-channel attack:
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…
CRYSTALS-Kyber is one of the public-key algorithms currently recommended by NIST as part of its post-quantum cryptography standardization process.
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…
A group of Swiss researchers have published an impressive security analysis of Threema.
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…
Yet another smartphone side-channel attack: “EarSpy: Spying Caller Speech and Identity through Tiny Vibrations of Smartphone Ear Speakers“:
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 …
Continue reading Recovering Smartphone Voice from the Accelerometer