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Cisco Fixed Critical RCE in Nexus 9000 Series Switches
Cisco patched CVE-2026-20212 (CVSS 9.8) in Silicon One-based Nexus 9000 switches, allowing unauthenticated remote root code execution via TCP ports 43210/43211.
Cisco patched CVE-2026-20212 (CVSS 9.8), a flaw in the Silicon One integration for Nexus 9000 Series switches that lets unauthenticated remote attackers execute code with root privileges. TCP ports 43210 and 43211 are exposed through the default Layer 3 VRF, and exploitation can also crash the S1HAL process, forcing device reloads. Cisco TAC discovered the flaw during a support case; PSIRT is not aware of public disclosure or malicious exploitation. Workarounds include infrastructure ACLs or blocking the exposed ports, alongside a Live Protect shield pending fixed NX-OS upgrades.
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Nintendo Switch Vulnerability Allows Attackers to Run Unauthorized Code on Your Console
Nintendo patched CVE-2026-82079, a CVSS 7.0 stack buffer overflow in original Switch local wireless allowing nearby code execution via QR-code workflows.
Nintendo patched CVE-2026-82079 (CVSS 4.0 base score 7.0, High), a stack-based buffer overflow in the original Switch's local wireless networking affecting firmware earlier than 23.0.0. An adjacent attacker must scan a QR code displayed by the console, via the Album "Send to Smartphone" feature or Mario Kart Live: Home Circuit, before crafted packets can corrupt memory and enable return-oriented programming for arbitrary code execution. EPSS is approximately 0.16%, Switch 2 is not affected, and Nintendo's advisory was published September 10, 2026.
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Train Smarter, Not Harder: Switching Signal-Guided Training in Active Learning
HybridAL is an active-learning training schedule that switches from retraining to fine-tuning on stabilization signals, saving up to 49% time.
Researchers find that choosing between retraining from scratch and fine-tuning is an exploitable decision variable in active learning: retraining helps in early rounds while fine-tuning is safer once the model trajectory stabilizes. HybridAL monitors an online stabilization signal using spectral exponent change and accuracy change, switching from retraining to fine-tuning after sustained stabilization. Across three encoder backbones and six text-classification tasks with five seeds each, HybridAL keeps endpoint macro-F1 non-inferior within a 0.010 margin, saves up to 49% of retraining time, and improves the time-calibration trade-off measured by negative log-likelihood.
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