Aggregator
CVE-2012-3282 | HP SAN/iQ prior 9.0 memory corruption (EDB-27555 / Nessus ID 64633)
What is Security Automation? Why Your Business Can’t Afford to Ignore It
The post What is Security Automation? Why Your Business Can’t Afford to Ignore It appeared first on AI Security Automation.
The post What is Security Automation? Why Your Business Can’t Afford to Ignore It appeared first on Security Boulevard.
Play
You must login to view this content
Play
You must login to view this content
Play
You must login to view this content
Play
You must login to view this content
Play
You must login to view this content
CVE-2025-38495 | Linux Kernel up to 6.1.146/6.6.99/6.12.39/6.15.7 Low Level Transport Driver allocation of resources (Nessus ID 252225 / WID-SEC-2025-1665)
CVE-2025-38496 | Linux Kernel up to 6.6.99/6.12.39/6.15.7 drivers/md/dm-bufio.c in_atomic buffer overflow (Nessus ID 251305 / WID-SEC-2025-1665)
CVE-2025-38497 | Linux Kernel up to 6.1.146/6.6.99/6.12.39/6.15.7 usb os_desc_qw_sign_store out-of-bounds (Nessus ID 253428 / WID-SEC-2025-1665)
CVE-2025-38492 | Linux Kernel up to 6.15.7 netfs race condition (WID-SEC-2025-1665)
CVE-2025-38493 | Linux Kernel up to 6.6.99/6.12.39/6.15.7 lib/string_helpers.c timerlat_dump_stack denial of service (Nessus ID 252218 / WID-SEC-2025-1665)
CVE-2025-38494 | Linux Kernel up to 6.1.146/6.6.99/6.12.39/6.15.7 Low Level Transport Driver hid_hw_raw_request buffer overflow (Nessus ID 252939 / WID-SEC-2025-1665)
CVE-2025-38489 | Linux Kernel up to 6.6.99/6.12.39/6.15.7 bpf_arch_text_poke denial of service (WID-SEC-2025-1665)
CVE-2025-38490 | Linux Kernel up to 6.6.99/6.12.39/6.15.7 net page_pool_put_full_page denial of service (Nessus ID 253428 / WID-SEC-2025-1665)
CVE-2025-38491 | Linux Kernel prior 6.12.40/6.15.8 mptcp net/mptcp/protocol.h __mptcp_do_fallback infinite loop (EUVD-2025-22872 / WID-SEC-2025-1665)
GhostBSD обновился — и теперь предлагает экспериментальный десктоп для фанатов macOS
Microsoft’s New AI Risk Assessment Framework – A Step Forward
Microsoft recently introduced a new framework designed to assess the security of AI models. It’s always encouraging to see developers weaving cybersecurity considerations into the design and deployment of emerging, disruptive technologies. Stronger security reduces the potential for harmful outcomes — and that’s a win for everyone.
It is wonderful to see that Microsoft leveraged its expertise to publish a clear framework for anyone to use.
While this framework provides a reasonable foundation for securing Large Language Model (LLM) AI deployments, it falls short when applied to more advanced AI systems — especially those with agentic capabilities. This limitation in applicability highlights a persistent problem in cybersecurity: tools and practices are often outdated or under-scaled, even before the industry has a chance to implement them.
AI is evolving at a breathtaking pace, and security adaptation consistently lags several steps behind. The release of this framework is a valuable step forward, but it’s critical to recognize that it’s just a step on a very long journey. The ongoing challenge is not to declare “mission accomplished,” but to treat security as a continuously adaptive process — always be looking to embrace the next best practices.
Risk governance for AI requires ongoing investment, flexibility, and willingness to evolve. Even then, the best we may achieve is keeping pace with evolving risks, maintaining as few steps behind as possible.
Paper Download: https://github.com/Azure/AI-Security-Risk-Assessment/blob/main/AI_Risk_Assessment_v4.1.4.pdf
The post Microsoft’s New AI Risk Assessment Framework – A Step Forward appeared first on Security Boulevard.