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Octopus and squid genes are weird.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
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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.
The experimental setup involves an electrostatic gun to generate a strong pulse signal that’s then sent to an antenna to transmit an electromagnetic field to the phone’s touchscreen, thereby causing the electrodes which act as antennas themselves to pick up the EMI.
Paper: “GhostTouch: Targeted Attacks on Touchscreens without Physical Touch“:
Abstract: Capacitive touchscreens have become the primary human-machine interface for personal devices such as smartphones and tablets. In this paper, we present GhostTouch, the first active contactless attack against capacitive touchscreens. GhostTouch uses electromagnetic interference (EMI) to inject fake touch points into a touchscreen without the need to physically touch it. By tuning the parameters of the electromagnetic signal and adjusting the antenna, we can inject two types of basic touch events, taps and swipes, into targeted locations of the touchscreen and control them to manipulate the underlying device. We successfully launch the GhostTouch attacks on nine smartphone models. We can inject targeted taps continuously with a standard deviation of as low as 14.6 x 19.2 pixels from the target area, a delay of less than 0.5s and a distance of up to 40mm. We show the real-world impact of the GhostTouch attacks in a few proof-of-concept scenarios, including answering an eavesdropping phone call, pressing the button, swiping up to unlock, and entering a password. Finally, we discuss potential hardware and software countermeasures to mitigate the attack.
Interesting paper by Lennart Maschmeyer: “The Subversive Trilemma: Why Cyber Operations Fall Short of Expectations“:
Abstract: Although cyber conflict has existed for thirty years, the strategic utility of cyber operations remains unclear. Many expect cyber operations to provide independent utility in both warfare and low-intensity competition. Underlying these expectations are broadly shared assumptions that information technology increases operational effectiveness. But a growing body of research shows how cyber operations tend to fall short of their promise. The reason for this shortfall is their subversive mechanism of action. In theory, subversion provides a way to exert influence at lower risks than force because it is secret and indirect, exploiting systems to use them against adversaries. The mismatch between promise and practice is the consequence of the subversive trilemma of cyber operations, whereby speed, intensity, and control are negatively correlated. These constraints pose a trilemma for actors because a gain in one variable tends to produce losses across the other two variables. A case study of the Russo-Ukrainian conflict provides empirical support for the argument. Qualitative analysis leverages original data from field interviews, leaked documents, forensic evidence, and local media. Findings show that the subversive trilemma limited the strategic utility of all five major disruptive cyber operations in this conflict.
Yet another adversarial ML attack:
Most deep neural networks are trained by stochastic gradient descent. Now “stochastic” is a fancy Greek word for “random”; it means that the training data are fed into the model in random order.
So what happens if the bad guys can cause the order to be not random? You guessed it—all bets are off. Suppose for example a company or a country wanted to have a credit-scoring system that’s secretly sexist, but still be able to pretend that its training was actually fair. Well, they could assemble a set of financial data that was representative of the whole population, but start the model’s training on ten rich men and ten poor women drawn from that set then let initialisation bias do the rest of the work.
Does this generalise? Indeed it does. Previously, people had assumed that in order to poison a model or introduce backdoors, you needed to add adversarial samples to the training data. Our latest paper shows that’s not necessary at all. If an adversary can manipulate the order in which batches of training data are presented to the model, they can undermine both its integrity (by poisoning it) and its availability (by causing training to be less effective, or take longer). This is quite general across models that use stochastic gradient descent.
Research paper.
A surprising number of websites include JavaScript keyloggers that collect everything you type as you type it, not just when you submit a form.
Researchers from KU Leuven, Radboud University, and University of Lausanne crawled and analyzed the top 100,000 websites, looking at scenarios in which a user is visiting a site while in the European Union and visiting a site from the United States. They found that 1,844 websites gathered an EU user’s email address without their consent, and a staggering 2,950 logged a US user’s email in some form. Many of the sites seemingly do not intend to conduct the data-logging but incorporate third-party marketing and analytics services that cause the behavior.
After specifically crawling sites for password leaks in May 2021, the researchers also found 52 websites in which third parties, including the Russian tech giant Yandex, were incidentally collecting password data before submission. The group disclosed their findings to these sites, and all 52 instances have since been resolved.
“If there’s a Submit button on a form, the reasonable expectation is that it does something—that it will submit your data when you click it,” says Güneş Acar, a professor and researcher in Radboud University’s digital security group and one of the leaders of the study. “We were super surprised by these results. We thought maybe we were going to find a few hundred websites where your email is collected before you submit, but this exceeded our expectations by far.”
Research paper.
Video of oval squid (Sepioteuthis lessoniana) changing color in reaction to their background. The research paper claims this is the first time this has been documented.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
Read my blog posting guidelines here.
Researchers are using the reflection of the smartphone in the pupils of faces taken as selfies to infer information about how the phone is being used:
For now, the research is focusing on six different ways a user can hold a device like a smartphone: with both hands, just the left, or just the right in portrait mode, and the same options in horizontal mode.
It’s not a lot of information, but it’s a start. (It’ll be a while before we can reproduce these results from Blade Runner.)
Research paper.
New research: “Are You Really Muted?: A Privacy Analysis of Mute Buttons in Video Conferencing Apps“:
Abstract: In the post-pandemic era, video conferencing apps (VCAs) have converted previously private spaces—bedrooms, living rooms, and kitchens—into semi-public extensions of the office. And for the most part, users have accepted these apps in their personal space, without much thought about the permission models that govern the use of their personal data during meetings. While access to a device’s video camera is carefully controlled, little has been done to ensure the same level of privacy for accessing the microphone. In this work, we ask the question: what happens to the microphone data when a user clicks the mute button in a VCA? We first conduct a user study to analyze users’ understanding of the permission model of the mute button. Then, using runtime binary analysis tools, we trace raw audio in many popular VCAs as it traverses the app from the audio driver to the network. We find fragmented policies for dealing with microphone data among VCAs—some continuously monitor the microphone input during mute, and others do so periodically. One app transmits statistics of the audio to its telemetry servers while the app is muted. Using network traffic that we intercept en route to the telemetry server, we implement a proof-of-concept background activity classifier and demonstrate the feasibility of inferring the ongoing background activity during a meeting—cooking, cleaning, typing, etc. We achieved 81.9% macro accuracy on identifying six common background activities using intercepted outgoing telemetry packets when a user is muted.
The paper will be presented at PETS this year.
News article.
Drawing inspiration from cephalopod skin, engineers at the University of California, Irvine invented an adaptive composite material that can insulate beverage cups, restaurant to-go bags, parcel boxes and even shipping containers.
[…]
“The metal islands in our composite material are next to one another when the material is relaxed and become separated when the material is stretched, allowing for control of the reflection and transmission of infrared light or heat dissipation,” said Gorodetsky. “The mechanism is analogous to chromatophore expansion and contraction in a squid’s skin, which alters the reflection and transmission of visible light.”
Chromatophore size changes help squids communicate and camouflage their bodies to evade predators and hide from prey. Gorodetsky said by mimicking this approach, his team has enabled “tunable thermoregulation” in their material, which can lead to improved energy efficiency and protect sensitive fingers from hot surfaces.
Research paper.
As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.
Read my blog posting guidelines here.
New paper: “Planting Undetectable Backdoors in Machine Learning Models“:
Abstract: Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. We show how a malicious learner can plant an undetectable backdoor into a classifier. On the surface, such a backdoored classifier behaves normally, but in reality, the learner maintains a mechanism for changing the classification of any input, with only a slight perturbation. Importantly, without the appropriate “backdoor key”, the mechanism is hidden and cannot be detected by any computationally-bounded observer. We demonstrate two frameworks for planting undetectable backdoors, with incomparable guarantees.
First, we show how to plant a backdoor in any model, using digital signature schemes. The construction guarantees that given black-box access to the original model and the backdoored version, it is computationally infeasible to find even a single input where they differ. This property implies that the backdoored model has generalization error comparable with the original model. Second, we demonstrate how to insert undetectable backdoors in models trained using the Random Fourier Features (RFF) learning paradigm or in Random ReLU networks. In this construction, undetectability holds against powerful white-box distinguishers: given a complete description of the network and the training data, no efficient distinguisher can guess whether the model is “clean” or contains a backdoor.
Our construction of undetectable backdoors also sheds light on the related issue of robustness to adversarial examples. In particular, our construction can produce a classifier that is indistinguishable from an “adversarially robust” classifier, but where every input has an adversarial example! In summary, the existence of undetectable backdoors represent a significant theoretical roadblock to certifying adversarial robustness.
EDITED TO ADD (4/20): Cory Doctorow wrote about this as well.
Sidebar photo of Bruce Schneier by Joe MacInnis.