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Talos discovers Microsoft kernel mode driver vulnerabilities that could lead to SYSTEM privileges; Seven other critical issues disclosed
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ZDI-26-685: Linux Kernel NFC NCI UART Driver Race Condition Local Privilege Escalation Vulnerability
ZDI discloses a race condition in the Linux kernel NFC NCI UART driver (CVE-2025-38416, CVSS 8.8) allowing local low-privileged attackers to escalate privileges.
ZDI advisory ZDI-26-685 covers a race condition vulnerability in the Linux kernel's NFC NCI UART driver, tracked as CVE-2025-38416 with a CVSS score of 8.8. A local attacker with the ability to execute low-privileged code can exploit the race to escalate privileges on affected systems. Exposure is limited to systems where the NFC NCI UART driver is present, and no exploitation is reported.
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ZDI-26-692: Linux Kernel eMPIA USB Device Driver Race Condition Code Execution Vulnerability
ZDI-26-692: race condition in Linux kernel eMPIA USB driver (CVE-2026-31583) lets physically present attackers execute code without authentication; CVSS 7.1.
ZDI advisory ZDI-26-692 discloses a race condition in the Linux kernel's eMPIA USB device driver that allows physically present attackers to execute arbitrary code on affected installations. Authentication is not required, but physical access to the target system is necessary. ZDI assigned a CVSS rating of 7.1 and CVE-2026-31583.
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ZDI-26-702: Linux Kernel usbnet Driver Race Condition Privilege Escalation Vulnerability
ZDI discloses Linux Kernel usbnet driver race condition (CVE-2025-22050) enabling physically present attackers to escalate privileges without authentication.
ZDI-26-702 covers a race condition privilege escalation vulnerability in the Linux Kernel usbnet driver, assigned CVE-2025-22050 with a CVSS rating of 7.1. A physically present attacker can escalate privileges on affected installations. Authentication is not required to exploit the vulnerability.
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Retrospectively Reverse-Engineering Apple's Neural Engine
A developer reverse-engineers Apple's M1 Neural Engine architecture, mapping compute cores, MAC datapaths, and schedulers to explain the NPU's decline as transformers displaced CNN workloads.
A developer who previously maintained a reverse-engineered Linux driver for Apple's Neural Engine (ANE) published a retrospective deep dive mapping the M1 ANE's full internal architecture: compute, datapath, scheduler, memory, and execution model. The M1 ANE has 16 compute cores with 128 FP16 (or 256 INT8) MAC lanes each, totaling 2048 parallel MAC lanes, using 32-bit Q16.16 fixed-point accumulation with FP16 readout and an accumulator that saturates at 2^15. The author argues the ANE's dataflow was architected around the predictable reuse patterns of 2017-era CNN workloads (dating to the A11 Bionic), which autoregressive transformer decode broke, limiting its usefulness for general ML. With Apple's M5 folding ANE cores into GPU cores to tout LLM performance, the post frames this as the beginning of the end for the standalone NPU.
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