Divide-by-zero bug in tsMuxer 2.6.16 allows attackers to cause a Denial of Service (DoS) by running the application with a crafted file.

A divide-by-zero issue was found in dwc2_handle_packet in hw/usb/hcd-dwc2.c in the hcd-dwc2 USB host controller emulation of QEMU. A malicious guest could use this flaw to crash the QEMU process on the host, resulting in a denial of service.

FFmpeg 4.2 is affected by a Divide By Zero issue via libavcodec/aaccoder, which allows a remote malicious user to cause a Denial of Service

FFmpeg 4.2 is affected by a Divide By Zero issue via libavcodec/lpc.h, which allows a remote malicious user to cause a Denial of Service.

FFmpeg 4.2 is affected by a Divide By Zero issue via libavcodec/aacpsy.c, which allows a remote malicious user to cause a Denial of Service.

FFmpeg 4.1.3 is affected by a Divide By Zero issue via libavcodec/ratecontrol.c, which allows a remote malicious user to cause a Denial of Service.

Mikrotik RouterOs before 6.47 (stable tree) in the /ram/pckg/advanced-tools/nova/bin/netwatch process. An authenticated remote attacker can cause a Denial of Service due to a divide by zero error.

Mikrotik RouterOs before 6.47 (stable tree) suffers from a divison by zero vulnerability in the /nova/bin/lcdstat process. An authenticated remote attacker can cause a Denial of Service due to a divide by zero error.

TensorFlow is an end-to-end open source platform for machine learning. A malicious user could trigger a division by 0 in `Conv3D` implementation. The implementation(https://github.com/tensorflow/tensorflow/blob/42033603003965bffac51ae171b51801565e002d/tensorflow/core/kernels/conv_ops_3d.cc#L143-L145) does a modulo operation based on user controlled input. Thus, when `filter` has a 0 as the fifth element, this results in a division by 0. Additionally, if the shape of the two tensors is not valid, an Eigen assertion can be triggered, resulting in a program crash. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc) does not validate all constraints specified in the op's contract(https://www.tensorflow.org/api_docs/python/tf/raw_ops/QuantizedBatchNormWithGlobalNormalization). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.