16 matches found
Linux Distros Unpatched Vulnerability : CVE-2026-42627
The Linux/Unix host has one or more packages installed that are impacted by a vulnerability without a vendor supplied patch available. - In Arm ArmNN through 2026-03-27, an integer overflow in TensorShape::GetNumElements in armnn/Tensor.cpp allows a crafted TFLite model file to bypass buffer size...
DEBIAN-CVE-2026-42627
In Arm ArmNN through 2026-03-27, an integer overflow in TensorShape::GetNumElements in armnn/Tensor.cpp allows a crafted TFLite model file to bypass buffer size validation and trigger a heap-based buffer over-read during model optimization. The overflow occurs when multiplying tensor dimensions...
CVE-2026-42627
In Arm ArmNN through 2026-03-27, an integer overflow in TensorShape::GetNumElements in armnn/Tensor.cpp allows a crafted TFLite model file to bypass buffer size validation and trigger a heap-based buffer over-read during model optimization. The overflow occurs when multiplying tensor dimensions...
CVE-2026-42627
In Arm ArmNN through 2026-03-27, an integer overflow in TensorShape::GetNumElements in armnn/Tensor.cpp allows a crafted TFLite model file to bypass buffer size validation and trigger a heap-based buffer over-read during model optimization. The overflow occurs when multiplying tensor dimensions...
CVE-2026-42627
In Arm ArmNN through 2026-03-27, an integer overflow in TensorShape::GetNumElements in armnn/Tensor.cpp allows a crafted TFLite model file to bypass buffer size validation and trigger a heap-based buffer over-read during model optimization. The overflow occurs when multiplying tensor dimensions...
CVE-2026-42627
In Arm ArmNN through 2026-03-27, an integer overflow in TensorShape::GetNumElements in armnn/Tensor.cpp allows a crafted TFLite model file to bypass buffer size validation and trigger a heap-based buffer over-read during model optimization. The overflow occurs when multiplying tensor dimensions...
PT-2026-42819
Name of the Vulnerable Software and Affected Versions Arm ArmNN versions prior to 2026-03-28 Description An integer overflow exists in the TensorShape::GetNumElements function within armnn/Tensor.cpp. This occurs when tensor dimensions are multiplied using 32-bit unsigned arithmetic without...
CVE-2026-42627
In Arm ArmNN through 2026-03-27, an integer overflow in TensorShape::GetNumElements in armnn/Tensor.cpp allows a crafted TFLite model file to bypass buffer size validation and trigger a heap-based buffer over-read during model optimization. The overflow occurs when multiplying tensor dimensions...
Important: Red Hat Security Advisory: Red Hat AI Inference Server Model Optimization Tools 3.3.3 (CUDA)
Red Hat AI Inference Server Model Optimization Tools 3.3.3 CUDA is now available. Red Hat® AI Inference Server Model Optimization Tools...
Important: Red Hat Security Advisory: Red Hat AI Inference Server Model Optimization Tools 3.3.1 (CUDA)
Red Hat AI Inference Server Model Optimization Tools 3.3.1 CUDA is now available. Red Hat® AI Inference Server Model Optimization Tools...
Important: Red Hat Security Advisory: Red Hat AI Inference Server Model Optimization Tools 3.2.2 (CUDA)
Red Hat AI Inference Server Model Optimization Tools 3.2.2 CUDA is now available. Red Hat® AI Inference Server Model Optimization Tools...
Important: Red Hat Security Advisory: Red Hat AI Inference Server Model Optimization Tools 3.2.2 (CUDA)
Red Hat AI Inference Server Model Optimization Tools 3.2.2 CUDA is now available. Red Hat® AI Inference Server Model Optimization Tools...
Important: Red Hat Security Advisory: Red Hat AI Inference Server Model Optimization Tools 3.2.5 (CUDA)
Red Hat AI Inference Server Model Optimization Tools 3.2.5 CUDA is now available. Red Hat® AI Inference Server Model Optimization Tools...
Important: Red Hat Security Advisory: Red Hat AI Inference Server Model Optimization Tools 3.2.2 (CUDA)
Red Hat AI Inference Server Model Optimization Tools 3.2.2 CUDA is now available. Red Hat® AI Inference Server Model Optimization Tools...
AI Summarization Optimization
These days, the most important meeting attendee isn’t a person: It’s the AI notetaker. This system assigns action items and determines the importance of what is said. If it becomes necessary to revisit the facts of the meeting, its summary is treated as impartial evidence. But clever meeting...
A Novel GPT-Based Framework for Anomaly Detection in System Logs
Identification of anomalous events within system logs constitutes a pivotal element within the frame- work of cybersecurity defense strategies. However, this process faces numerous challenges, including the management of substantial data volumes, the distribution of anomalies, and the precision o...