Aggregator
CVE-2025-29662 | LandChat 3.25.12.18 Core Application Remote Code Execution
CVE-2021-47669 | Linux Kernel up to 4.14.217/4.19.170/5.4.92/5.10.10 vxcan netif_rx_ni use after free
CVE-2025-28009 | Dietiqa App 1.0.20 progress-body-weight.php u sql injection
CVE-2025-28101 | flaskBlog 2.6.1 POST /post/{postTitle} cross-site request forgery (Issue 130)
CVE-2025-32415 | xmlsoft libxml2 up to 2.13.7/2.14.1 XML Document xmlschemas.c xmlSchemaIDCFillNodeTables improper validation of specified quantity in input (Issue 890)
CVE-2024-42177 | HCL MyXalytics 6.3 TLS Protocol inadequate encryption (KB0120504)
CVE-2024-53924 | Pycel up to 1.0b30 Formula code injection
CVE-2020-36789 | Linux Kernel up to 5.9.8 net/core/skbuff.c can_get_echo_skb reference count
CVE-2021-47671 | Linux Kernel up to 5.14.18/5.15.2 es58x_rx_err_msg memory leak
CVE-2021-47670 | Linux Kernel up to 4.19.170/5.4.92/5.10.10 peak_usb_netif_rx_ni use after free
CVE-2021-47668 | Linux Kernel up to 5.10.10 netif_rx_ni use after free
CVE-2025-2947 | IBM i 7.6 insecure preserved inherited permissions
China-linked APT Mustang Panda upgrades tools in its arsenal
Windows NTLM hash leak flaw exploited in phishing attacks on governments
Ransomware Attacks Surge 126%, Targeting Consumer Goods and Services Sector
The cybersecurity landscape witnessed a dramatic escalation in ransomware attacks, marking a concerning trend for global businesses. According to a recent analysis by Check Point Research, ransomware incidents surged by an alarming 126% compared to the same period in 2024. This surge has not been indiscriminate; the consumer goods & services sector emerged as the […]
The post Ransomware Attacks Surge 126%, Targeting Consumer Goods and Services Sector appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.
NIST’s adversarial ML guidance: 6 action items for your security team
The National Institute of Standards and Technology’s latest guidance, on how to secure artificial intelligence (AI) applications against manipulation and attacks achieved with adversarial machine learning (ML), represents a major step toward establishing a standard framework for understanding and mitigating the growing threats to AI applications, but it's still insufficient. Fortunately, there are six steps your organization can take right now to address adversarial ML vulnerabilities.
The post NIST’s adversarial ML guidance: 6 action items for your security team appeared first on Security Boulevard.