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NFCShare Android malware spreads via fake banking app updates on GitHub
New variants of the NFCShare Android malware are being distributed as fake updates for legitimate banking apps hosted on GitHub.
The malware has evolved and is now targeting customers of multiple banks and financial institutions across Europe in a phishing campaign aimed at stealing payment card data.
After tricking victims with a fake verification screen to place the cards near the mobile device’s near-field communication (NFC) chip, NFCShare reads the information using Android’s IsoDep interface and EMV commands.
Gogs patches critical zero-day enabling remote code execution
Gogs has patched a critical security zero-day flaw that can allow attackers to compromise Internet-facing instances and access any repositories (including private ones).
This argument injection vulnerability has yet to be assigned a CVE ID, can only be exploited by authenticated attackers without admin privileges, and affects all Gogs releases up to and including 0.14.2 and 0.15.0+dev.
They can exploit this vulnerability to compromise the targeted server, read any repository (including private repos), steal credentials, move laterally to other systems on the network, and alter any hosted source code.
Over 20,000 Instagram accounts stolen in Meta AI support hack
Meta has revealed that 20,225 Instagram users had their accounts hijacked in a recent incident where attackers used Meta’s AI-powered support system to reset passwords.
As BleepingComputer reported one week ago, the threat actors exploited a flaw in the company’s High Touch Support (HTS) tool, an AI-assisted support system that helps users regain access after being locked out of their Instagram accounts.
By exploiting the fact that HTS didn’t verify whether email addresses were associated with the targeted Instagram accounts, they obtained password reset links that allowed them to log in and hijack accounts without two-factor authentication (2FA) enabled.
Ultra-thin MoS₂ computer packs 1,400 transistors onto one chip
The rapid advancement and diffusion of artificial intelligence (AI) systems, such as the machine learning models underpinning the functioning of ChatGPT, Gemini and similar platforms, have posed new demands on the electronics engineering industry. In fact, these systems are computationally intensive and consume substantial power, particularly when running on existing devices.
Electronics engineers worldwide have thus been trying to develop new hardware systems that can run machine learning algorithms more energy efficiently, without adversely affecting their performance. One promising approach for reducing power consumption entails the use of two-dimensional (2D) semiconductors, ultrathin materials that have already proved promising for the development of smaller electronics.
Researchers at Nanjing University, Suzhou Laboratory and Huawei Technologies Co. Ltd. recently developed and fabricated a fully functional computer based on the 2D semiconductor molybdenum disulfide (MoS₂).
Organic transistor unites memory, signal processing and light emission below 3.5 V
Seoul National University researchers have developed an ultra-low-voltage electrochemical organic light-emitting transistor that can simultaneously perform signal processing, memory and light emission within a single semiconductor device. By introducing an ion-transport enhancer into the light-emitting polymer semiconductor channel, the team enabled electric-double-layer formation at the drain electrode interface, allowing efficient electron injection without relying on the high voltages or unstable n-type doping used in conventional approaches.
As a result, the device maintained a simple single-active-layer structure while achieving both low-voltage operation and wide, spatially pinned light emission, together with neuromorphic signal-processing functionality.
The work is published in the journal Nature Materials.
Neutron star merger simulations gain new precision with AI-driven r-process heating
Using a novel simulation model based on machine learning, an international research team at GSI/FAIR has succeeded in gaining a deeper understanding of element formation in stellar events such as neutron star mergers. For the first time, the scientists used deep learning with a neural network to model the energy release during r-process nucleosynthesis in hydrodynamic simulations. The results are published in the journal Physical Review D.
Many of the chemical elements we know are created in massive stellar events such as exploding stars or neutron star mergers. These events release incredible amounts of energy, allowing for the production of heavy nuclides. One key nuclear production process is the so-called rapid neutron-capture process, or r-process, in which free neutrons are captured by existing nuclei and converted into protons—thus creating larger, heavier atomic nuclei.
“Researchers around the world strive to make these complex reactions understandable through theoretical simulations. However, modeling all parameters requires incredible computing power, which is why the models often have to be simplified,” said Dr. Oliver Just, first author of the publication and a researcher in the Nuclear Astrophysics & Structure Department at GSI/FAIR. “Our new model, RHINE, which uses artificial intelligence, offers an efficient alternative.”
