Aptar Active Material Science Secures USPTO Allowance for N-Sorb℠ Technology, Establishing First-of-Its-Kind Packaging-Delivered Approach to Nitrosamine Risk Mitigation - Aptar

Executive SummaryOn October 15, 2026, unauthorized access to application identities within the ConnectWise Cloud Backup (formerly SkyKick Cloud Backup) environment enabled attackers to compromise at least 162 Microsoft 365 tenants. The breach was executed via a supply-chain attack, leveraging a stolen application credential to authenticate as a trusted service principal in customer environments. This allowed the attacker to inherit all permissions previously granted to the application, including broad access to emails, files, and directory data. The attack was confirmed by multiple independent sources, including Microsoft, Huntress, and ConnectWise. The incident led to the suspension of ConnectWise Cloud Backup and related services, with ongoing investigations and remediation efforts. There is currently no evidence that backup archives were accessed or exfiltrated, but the full scope of tenant impact remains under review. The attack bypassed multi-factor authentication (MFA) due to its use of application credentials rather than user logins.

Researchers at Google Quantum AI and Cornell University have demonstrated a new error mitigation technique that surpasses existing methods by operating entirely in classical post-processing. Their work introduces virtual error cancellation, a method that reduces sampling overhead by using only syndrome records from quantum circuits, bypassing the need for prior noise characterization and modified circuits. Numerical simulations using a distance-7 rotated surface code show the full-stack dual-decoder algorithm achieves more than three orders of magnitude of suppression in error rate compared to standard decoding. This approach operates entirely through classical post-processing, offering a streamlined pathway to more reliable computation. The core of this advancement lies in the ability to avoid the typical requirements of noise learning and circuit alterations, both of which add complexity and overhead to quantum error mitigation.