In video decoder, there is a possible improper input validation. This could lead to local denial of service with no additional execution privileges needed

Insufficient input validation in Kernel Mode module for Intel(R) Graphics Driver before version 25.20.100.6519 may allow an authenticated user to potentially enable denial of service via local access.

Improper input validation in AMD μProf could allow an attacker to perform a write to an invalid address, potentially resulting in denial of service.

Insufficient input validation in i40e driver for Intel(R) Ethernet 700 Series Controllers versions before 2.8.43 may allow an authenticated user to potentially enable a denial of service via local access.

Redis is an open source, in-memory database that persists on disk. Authenticated users can use the `HINCRBYFLOAT` command to create an invalid hash field that will crash Redis on access in affected versions. This issue has been addressed in in versions 7.0.11, 6.2.12, and 6.0.19. Users are advised to upgrade. There are no known workarounds for this issue.

Insufficient input validation in i40e driver for Intel(R) Ethernet 700 Series Controllers versions before 7.0 may allow an authenticated user to potentially enable a denial of service via local access.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a segmentation fault in `tf.raw_ops.MaxPoolGrad` caused by missing validation. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/maxpooling_op.cc) misses some validation for the `orig_input` and `orig_output` tensors. The fixes for CVE-2021-29579 were incomplete. We have patched the issue in GitHub commit 136b51f10903e044308cf77117c0ed9871350475. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

NVIDIA Virtual GPU Manager contains a vulnerability in the vGPU plugin, in which an input data size is not validated, which may lead to tampering or denial of service. This affects vGPU version 8.x (prior to 8.5), version 10.x (prior to 10.4) and version 11.0.

The bundle management subsystem within OpenHarmony-v3.1.4 and prior versions has a null pointer reference vulnerability which local attackers can exploit this vulnerability to cause a DoS attack to the system when installing a malicious HAP package.

Improper input validation in the Intel(R) Retail Edge Mobile Android application before version 3.0.301126-RELEASE may allow an authenticated user to potentially enable denial of service via local access.

Issue of buffer overflow caused by insufficient data verification in the kernel acceleration module. Impact: Successful exploitation of this vulnerability may affect availability.

Issue of buffer overflow caused by insufficient data verification in the kernel gyroscope module. Impact: Successful exploitation of this vulnerability may affect availability.

Issue of buffer overflow caused by insufficient data verification in the kernel drop detection module. Impact: Successful exploitation of this vulnerability may affect availability.

Input verification vulnerability in the home screen module. Impact: Successful exploitation of this vulnerability may affect availability.

Communication Wi-Fi subsystem within OpenHarmony-v3.1.4 and prior versions, OpenHarmony-v3.0.7 and prior versions has a null pointer reference vulnerability which local attackers can exploit this vulnerability to cause the current application to crash.

Parameter corruption in NDIS filter driver in Intel Online Connect Access 1.9.22.0 allows an attacker to cause a denial of service via local access.

In multiple functions of multiple files, there is a possible way to make the device unusable due to improper input validation. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-12 Android-12L Android-13Android ID: A-268193777

In multiple functions of JobStore.java, there is a possible way to cause a crash on startup due to improper input validation. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-12 Android-12L Android-13Android ID: A-246542285

IBM AIX's 7.3 Python implementation could allow a non-privileged local user to exploit a vulnerability to cause a denial of service. IBM X-Force ID: 267965.

NVIDIA vGPU manager contains a vulnerability in the vGPU plugin, in which input data is not validated, which may lead to unexpected consumption of resources, which in turn may lead to denial of service. This affects vGPU version 8.x (prior to 8.6) and version 11.0 (prior to 11.3).

In TeleService, there is a possible system crash due to improper input validation. This could lead to local denial of service with no additional execution privileges needed

In TeleService, there is a possible system crash due to improper input validation. This could lead to local denial of service with no additional execution privileges needed

In TeleService, there is a possible system crash due to improper input validation. This could lead to local denial of service with no additional execution privileges needed

RedisBloom adds a set of probabilistic data structures to Redis. Starting in version 2.0.0 and prior to version 2.4.7 and 2.6.10, authenticated users can use the `CF.RESERVE` command to trigger a runtime assertion and termination of the Redis server process. The problem is fixed in RedisBloom 2.4.7 and 2.6.10.

IBM Financial Transaction Manager for SWIFT Services for Multiplatforms 3.2.4 could allow an authenticated user to lock additional RM authorizations, resulting in a denial of service on displaying or managing these authorizations. IBM X-Force ID: 240034.

A vulnerability in the interprocess communication (IPC) channel of Cisco AnyConnect Secure Mobility Client for Windows could allow an authenticated, local attacker to cause a denial of service (DoS) condition on an affected device. To exploit this vulnerability, the attacker would need to have valid credentials on the Windows system. The vulnerability is due to insufficient validation of user-supplied input. An attacker could exploit this vulnerability by sending a crafted IPC message to the AnyConnect process on an affected device. A successful exploit could allow the attacker to stop the AnyConnect process, causing a DoS condition on the device. To exploit this vulnerability, the attacker would need to have valid credentials on the Windows system.

NVIDIA GPU Display Driver for Windows contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler, where improper input validation of a display-related data structure may lead to denial of service.

In registerPhoneAccount of PhoneAccountRegistrar.java, there is a possible way to prevent the user from selecting a phone account due to improper input validation. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10 Android-11 Android-12 Android-12LAndroid ID: A-217934478

A flaw was found in the Linux kernel. A denial of service flaw may occur if there is a consecutive request of the NVME_IOCTL_RESET and the NVME_IOCTL_SUBSYS_RESET through the device file of the driver, resulting in a PCIe link disconnect.

Improper input validation in firmware for Intel(R) SPS before version SPS_E3_04.01.04.700.0 may allow an authenticated user to potentially enable denial of service via local access.

QEMU (aka Quick Emulator) built with a VMWARE VMXNET3 paravirtual NIC emulator support is vulnerable to crash issue. It occurs when a guest sends a Layer-2 packet smaller than 22 bytes. A privileged (CAP_SYS_RAWIO) guest user could use this flaw to crash the QEMU process instance resulting in DoS.

Transient Denial-of-service in Automotive due to improper input validation while parsing ELF file.

NVIDIA GPU Display Driver for Windows contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape, where the product receives input or data, but does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly, which may lead to denial of service.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SparseTensorDenseAdd` does not fully validate the input arguments. In this case, a reference gets bound to a `nullptr` during kernel execution. This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Improper validation for loop variable received from firmware can lead to out of bound access in WLAN function while iterating through loop in Snapdragon Auto, Snapdragon Compute, Snapdragon Consumer Electronics Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music in APQ8053, APQ8096AU, APQ8098, MDM9640, MSM8996AU, MSM8998, QCA6574AU, QCN7605, QCS405, QCS605, SDA845, SDM845, SDX20

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could be greater than 1 but code was always assuming sub-unit scaling. Thus, since code was calling `QuantizeMultiplierSmallerThanOneExp`, the `TFLITE_CHECK_LT` assertion would trigger and abort the process. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.LSTMBlockCell` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code does not validate the ranks of any of the arguments to this API call. This results in `CHECK`-failures when the elements of the tensor are accessed. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.histogram_fixed_width` is vulnerable to a crash when the values array contain `Not a Number` (`NaN`) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If `values` contains `NaN` then the result of the division is still `NaN` and the cast to `int32` would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, multiple TensorFlow operations misbehave in eager mode when the resource handle provided to them is invalid. In graph mode, it would have been impossible to perform these API calls, but migration to TF 2.x eager mode opened up this vulnerability. If the resource handle is empty, then a reference is bound to a null pointer inside TensorFlow codebase (various codepaths). This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Improper input validation in some Intel(R) Thunderbolt(TM) controllers may allow an authenticated user to potentially enable denial of service via local access.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the `tf.compat.v1.signal.rfft2d` and `tf.compat.v1.signal.rfft3d` lack input validation and under certain condition can result in crashes (due to `CHECK`-failures). Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Improper input validation in some Intel(R) Graphics Drivers for Windows* before version 26.20.100.7212 and before Linux kernel version 5.5 may allow a privileged user to potentially enable a denial of service via local access.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.DeleteSessionTensor` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.TensorSummaryV2` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.ragged.constant` does not fully validate the input arguments. This results in a denial of service by consuming all available memory. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.GetSessionTensor` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

NVIDIA GPU Display Driver for Windows contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape, where improper input validation can cause denial of service.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.QuantizedConv2D` does not fully validate the input arguments. In this case, references get bound to `nullptr` for each argument that is empty. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.Conv3DBackpropFilterV2` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code does not validate that the `filter_sizes` argument is a vector. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.StagePeek` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `index` is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.