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.

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.

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.