Microsoft released mitigations for YellowKey, a publicly disclosed BitLocker bypass tracked as CVE-2026–45585 with a CVSS score of 6.8.
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Microsoft shares mitigation for YellowKey Windows zero-day
Microsoft has shared mitigations for YellowKey, a recently disclosed Windows BitLocker zero-day vulnerability that grants access to protected drives.
The security flaw was disclosed last week by an anonymous security researcher known as ‘Nightmare Eclipse,’ who described it as a backdoor and published a proof-of-concept (PoC) exploit.
Nightmare Eclipse said that exploiting this zero-day involves placing specially crafted ‘FsTx’ files on a USB drive or EFI partition, rebooting into WinRE, and then triggering a shell with unrestricted access to the BitLocker-protected storage volume by holding down the CTRL key.
Ukraine identifies infostealer operator tied to 28,000 stolen accounts
The Ukrainian cyberpolice, working in conjunction with U.S. law enforcement, has identified an 18-year-old man from Odesa suspected of running an infostealer malware operation targeting users of an online store in California.
According to the Ukrainian police, the threat actor used information-stealing malware between 2024 and 2025 to infect users’ devices and steal browser sessions and account credentials.
Infostealers are a popular type of malware that harvests sensitive data, including passwords, browser cookies, session tokens, crypto wallets, and payment information, from infected devices and sends it to cybercriminals for account theft, fraud, and resale.
Exploit released for new PinTheft Arch Linux root escalation flaw
A recently patched Linux privilege escalation vulnerability now has a publicly available proof-of-concept (PoC) exploit that allows local attackers to gain root privileges on Arch Linux systems.
The vulnerability, named PinTheft by the V12 security team and still waiting to be assigned a CVE ID for easier tracking, exists in the Linux kernel’s RDS (Reliable Datagram Sockets) and was patched earlier this month.
“PinTheft is a Linux local privilege escalation exploit for an RDS zerocopy double-free that can be turned into a page-cache overwrite through io_uring fixed buffers,” V12 said in a Tuesday advisory.
An OpenAI model has disproved a central conjecture in discrete geometry
Today, we share a breakthrough on the unit distance problem. Since Erdős’s original work, the prevailing belief has been that the “square grid” constructions depicted further below were essentially optimal for maximizing the number of unit-distance pairs. An internal OpenAI model has disproved this longstanding conjecture, providing an infinite family of examples that yield a polynomial improvement. The proof has been checked by a group of external mathematicians. They have also written a companion paper explaining the argument and providing further background and context for the significance of the result.
The result is also notable for how it was found. The proof came from a new general-purpose reasoning model, rather than from a system trained specifically for mathematics, scaffolded to search through proof strategies, or targeted at the unit distance problem in particular. As part of a broader effort to test whether advanced models can contribute to frontier research, we evaluated it on a collection of Erdős problems. In this case, it produced a proof resolving the open problem.
This proof is an important milestone for the math and AI communities. It marks the first time that a prominent open problem, central to a subfield of mathematics, has been solved autonomously by AI. It also demonstrates the depth of reasoning these systems now support. Mathematics provides a particularly clear testbed for reasoning: the problems are precise, potential proofs can be checked, and a long argument only works if the reasoning holds together from beginning to end. The method by which the problem was solved is also notable. The proof brings unexpected, sophisticated ideas from algebraic number theory to bear on an elementary geometric question.
AI atlas reveals hidden whole-body-damage caused by obesity
Obesity affects far more than metabolism and fat storage. It alters immune activity, nerve structure, and tissue organization across multiple organ systems, increasing the risk of diseases including type 2 diabetes, cardiovascular disease, stroke, neuropathy and cancer. Yet despite these systemic effects, researchers have lacked tools capable of studying disease-associated changes across the entire body in intact organisms and at high resolution.
A team led by Prof. Ali Ertürk, Director of the Institute for Biological Intelligence (iBIO) at Helmholtz Munich and Professor at the LMU, has now developed MouseMapper, a suite of foundation-model-based deep-learning algorithms designed to analyze whole-body biological imaging data. The framework automatically segments 31 organs and tissue types while quantitatively mapping nerves and immune cells throughout the body, enabling comprehensive multi-system analysis in intact mice.
“MouseMapper is built on a foundation model, which means it generalizes far beyond the data it was originally trained on,” says Ying Chen, co-first author of the study published in Nature.
Physicists confirm ‘negative time’ is real by asking the atoms themselves
A new experiment confirms that photons passing through a cloud of atoms can spend a negative amount of time there, and the atoms themselves are the ones saying so.
Can Aging Be Reversed?
What happens when biology starts behaving less like fate and more like software?
Some of the most controversial ideas in longevity research sound unbelievable at first… until the data starts forcing better questions.
Scientists predict winter weather months ahead using new method
Scientists have developed a new statistical model that predicts winter weather up to six months in advance by forecasting the behavior of the stratospheric polar vortex. [ https://www.labroots.com/trending/earth-and-the-environment/…g-method-2](https://www.labroots.com/trending/earth-and-the-environment/…g-method-2)
Can weather forecasts speed up predictions to help better prepare for inclement weather? This is what a recent study published in Journal of Geophysical Research Atmospheres hopes to address as an international team of scientists from Florida State University and China investigated a new method for providing better predictions of winter weather forecasts. This study has the potential to help sci better understand winter weather patterns and provide more in-depth and accurate predictions, enabling communities to better prepare for worst case scenarios.
This study is a secondary study in a series for this team, who published a first study also in the Journal of Geophysical Research Atmospheres focusing on the yearly weather patterns of the Northern Hemisphere stratospheric polar vortex (SPV). For this study, the researchers focused on developing a new method for predicting SPV weather patterns months in advance of the winter season.
Using a series of statistical models involving historical atmospheric data, the researchers ascertained to produce a statistical model capable of predicting winter weather patterns months in advance. In the end, the models demonstrated that forecasts could be made up to six months in advance.