This week’s ThreatsDay Bulletin tracks how attackers keep reshaping old tools and finding new angles in familiar systems. Small changes in tactics are stacking up fast, and each one hints at where the next big breach could come from.
From shifting infrastructures to clever social hooks, the week’s activity shows just how fluid the threat landscape has become.
Here’s the full rundown of what moved in the cyber world this week.
An automated campaign is targeting multiple VPN platforms, with credential-based attacks being observed on Palo Alto Networks GlobalProtect and Cisco SSL VPN.
On December 11, threat monitoring platform GreyNoise observed the number of login attempts aimed at GlobalProtect portals peaked at 1.7 million during a period of 16 hours.
Collected data showed that the attacks originated from more than 10,000 unique IP addresses and were aimed at infrastructure located in the United States, Mexico, and Pakistan.
Over the past decades, roboticists have introduced a wide range of systems that can effectively tackle some real-world problems. Most of these robots, however, often perform poorly on tasks that they were not trained on, particularly those that entail manipulating previously unseen objects or handling objects that were encountered before in new ways.
Researchers at the Robot Learning Lab at Imperial College London recently developed a new imitation learning approach that could allow robots to successfully learn new tasks faster and without requiring substantial training data. Using this method, which was introduced in a paper published in Science Robotics, they were able to train a robotic arm to complete 1,000 different tasks in a single day.
“The research was initially inspired by our prior work on trajectory transfer, where we introduced a method that proved robust and efficient for teaching robots single tasks,” Kamil Dreczkowski and Pietro Vitiello, co-authors of the paper, told Tech Xplore.
Researchers in the Department of Theoretical Physics at Tata Institute of Fundamental Research (TIFR), Mumbai, have discovered that instead of manipulating every component or modifying interactions in a many-body system, occasionally resetting just a small fraction can reshape how the entire system behaves, including how it transitions from one phase to another.
This counterintuitive approach, called subsystem resetting, offers a powerful, universal control strategy to tune collective behavior in complex systems ranging from magnets to neural networks.
This work by Anish Acharya, Rupak Majumder, and Prof. Shamik Gupta has been published in Physical Review Letters.
This new AI acts like a digital scientist, turning messy data into simple rules that explain how the world really works.
French authorities arrested a 22-year-old suspect on Tuesday for a cyberattack that targeted France’s Ministry of the Interior earlier this month.
In a statement issued by Public Prosecutor Laure Beccuau, officials said the suspected hacker was arrested on December 17, 2025, as part of an investigation into the attack.
“A person was arrested on December 17, 2025, as part of the investigation opened by the cybercrime unit of the Paris public prosecutor’s office, on charges including unauthorized access to an automated personal data processing system implemented by the State, committed by an organized group, following the cyberattack against the Ministry of the Interior,” reads the statement translated into English.
Artificial neurons – tiny electronic circuits that replicate the way brain cells communicate – lie at the heart of neuromorphic computing, a field aiming to bring human-like intelligence to machines.
Despite rapid progress, today’s artificial neurons can only perform fixed tasks, each serving a narrow role. Thousands must be combined to replicate simple brain functions – a costly, energy-hungry process compared with the brain’s effortless adaptability.
Researchers in the Nanoscience Center at the University of Jyväskylä, Finland, have developed a pioneering computational model that could expedite the use of nanomaterials in biomedical applications. The study presented the first generalizable machine-learning framework capable of predicting how proteins interact with ligand-stabilized gold nanoclusters, materials widely employed in bioimaging, biosensing, and targeted drug delivery.
The adsorption of proteins onto nanomaterial surfaces is fundamental to many biological applications, including bioimaging and biosensing to targeted drug delivery. Gold nanoclusters, in particular, have attracted attention thanks to their biocompatibility and tunable optical properties. Yet existing studies that predict how proteins interact with these ligand-protected nanostructures often focus on isolated cases, leaving researchers without a unified model to guide design.
“This gap has created a clear need for general, scalable models capable of capturing the underlying rules of protein–nanocluster binding,” specifies Postdoctoral Researcher Brenda Ferrari from the University of Jyväskylä
Although they’re constantly improving, robots aren’t necessarily known for their gentle touch. A new robotic system from MIT and Stanford takes a unique stab at changing that, with a robot that uses vine-like tendrils to do its lifting.
The system the engineers developed consists of a series of pneumatic tubes that deploy from a pressurized box on one side of a robotic arm, use air pressure to snake under or around a specific object, then rejoin the arm on the other side where they are clamped in place. Once clamped, the arm itself can move, or the tube can be wound up to lift or rotate the object in its grasp. The ability to deploy the tubes and then recapture them is the real breakthrough here, improving on previous vine-based robots by allowing the system to close its own loops.
“People might assume that in order to grab something, you just reach out and grab it,” says study co-author Kentaro Barhydt, from MIT’s Department of Mechanical Engineering. “But there are different stages, such as positioning and holding. By transforming between open and closed loops, we can achieve new levels of performance by leveraging the advantages of both forms for their respective stages.”