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CISA Adds Two Known Exploited Vulnerabilities to Catalog

 CISA has added two new vulnerabilities to its Known Exploited Vulnerabilities (KEV) Catalog, based on evidence of active exploitation. CVE-2026-5430 WSO2 Multiple Products Path Traversal Vulnerability CVE-2026-71362 Adobe Commerce and Magento Incorrect Authorization Vulnerability These types of vulnerabilities are a frequent attack vector for malicious cyber actors and pose significant risks to the federal enterprise. …

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Cyber Security

Siemens Mendix Runtime (Update A)

View CSAF Summary This advisory is revoked. Re-investigation confirmed the reported behavior is expected platform configuration and does not expose the protected attribute. The following versions of Siemens Mendix Runtime are affected: Siemens Mendix Runtime vers:all/* (CVE-2026-7891) CVSS Vendor Equipment Vulnerabilities v3 9.1 Siemens Siemens Mendix Runtime  Insecure Inherited Permissions Background Critical Infrastructure Sectors: Critical…

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Cyber Security

Botslab G980H Dashcams

View CSAF Summary Successful exploitation of these vulnerabilities could allow an attacker to bypass authentication controls, gain unauthorized access to sensitive data and privileged device functionality, modify device configuration, disrupt device operation. The following versions of Botslab G980H Dashcams are affected: G980H dash cam series 30010_QHG980HN5294SysFW+ (CVE-2026-84399, CVE-2026-82566, CVE-2026-85496, CVE-2026-77967, CVE-2026-88761, CVE-2026-82716, CVE-2026-84403, CVE-2026-75558, CVE-2026-81630,…

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Apps

Eufy Omni C20, Omni X10 Pro

View CSAF Summary Successful exploitation of these vulnerabilities could allow an attacker to run system level commands or execute arbitrary code. The following versions of Eufy Omni C20, Omni X10 Pro are affected: Omni C20 <1.6.4 (CVE-2026-93289, CVE-2026-93290, CVE-2026-93291) Omni X10 Pro <1.6.4 (CVE-2026-93289) CVSS Vendor Equipment Vulnerabilities v3 9.4 Eufy Eufy Omni C20, Omni…

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Apps

A Practical Recipe for Semi-Supervised Federated ASR: Online Pseudo-Labels with Server Update Stabilization

Semi-supervised federated learning (SSFL) trains models on clients’ unlabeled data using a teacher to generate pseudo-labels, with a small labeled seed dataset on the server. Automatic Speech Recognition (ASR) is particularly fragile here: pseudo-label errors compound across the output sequence and across training rounds into divergence, leaving a large gap to fully-supervised FL. We show…

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