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

in OpenHarmony v4.1.0 and prior versions allow a local attacker cause DOS through improper input.

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

An exploitable local denial-of-service vulnerability exists in the privileged helper tool of GOG Galaxy's Games, version 1.2.47 for macOS. An attacker can send malicious data to the root-listening service, causing the application to terminate and become unavailable.

An Improper Input Validation vulnerability in the Packet Forwarding Engine (PFE) of Juniper Networks Junos OS Evolved allows a local, low-privileged attacker to cause a Denial of Service (DoS). When a specific "clear" command is run, the Advanced Forwarding Toolkit manager (evo-aftmand-bt or evo-aftmand-zx) crashes and restarts. The crash impacts all traffic going through the FPCs, causing a DoS. Running the command repeatedly leads to a sustained DoS condition. This issue affects Junos OS Evolved:  * All versions before 20.4R3-S9-EVO,  * from 21.2-EVO before 21.2R3-S7-EVO,  * from 21.3-EVO before 21.3R3-S5-EVO,  * from 21.4-EVO before 21.4R3-S6-EVO,  * from 22.1-EVO before 22.1R3-S4-EVO,  * from 22.2-EVO before 22.2R3-S3-EVO,  * from 22.3-EVO before 22.3R3-S3-EVO,  * from 22.4-EVO before 22.4R3-EVO, * from 23.2-EVO before 23.2R2-EVO.

An Improper Input Validation vulnerability in the 802.1X Authentication (dot1x) Daemon of Juniper Networks Junos OS allows a local, low-privileged attacker with access to the CLI to cause a Denial of Service (DoS). On running a specific operational dot1x command, the dot1x daemon crashes. An attacker can cause a sustained DoS condition by running this command repeatedly. When the crash occurs, the authentication status of any 802.1x clients is cleared, and any authorized dot1x port becomes unauthorized. The client cannot re-authenticate until the dot1x daemon restarts. This issue affects Junos OS: * All versions before 20.4R3-S10; * 21.2 versions before 21.2R3-S7; * 21.4 versions before 21.4R3-S6; * 22.1 versions before 22.1R3-S5; * 22.2 versions before 22.2R3-S3; * 22.3 versions before 22.3R3-S2; * 22.4 versions before 22.4R3-S1; * 23.2 versions before 23.2R2.

The "nickname" field within Savoir-faire Linux's Jami application is susceptible to a failed state when a user inserts special characters into the field. When present, these special characters, make it so the application cannot create the signature for the user and results in a local denial of service to the application. 

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.

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.

In several functions of PhoneAccountRegistrar.java, there is a possible way to prevent an access to emergency services 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-256819769

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 DevicePolicyManager.java, there is a possible way to prevent enabling the Find my Device feature due to improper input validation. This could lead to local denial of service with User execution privileges needed. User interaction is not needed for exploitation.

Improper input validation in the API for Intel(R) Graphics Driver versions before 26.20.100.7209 may allow an authenticated user to potentially enable denial of service via local access.

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

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.

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

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.

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.

Data verification vulnerability in the HiView module. Impact: Successful exploitation of this vulnerability may affect availability.

Apple Bonjour before 2011 allows a crash via a crafted multicast DNS packet.

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.

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.UnsortedSegmentJoin` 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 `num_segments` is a positive scalar but there is no validation. Since this value is used to allocate the output tensor, a negative value would result in a `CHECK`-failure (assertion failure), as per TFSA-2021-198. 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.

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.UnsortedSegmentJoin` 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 `num_segments` 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.

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.

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.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, 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.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.

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.

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.SparseTensorToCSRSparseMatrix` 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 `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this. 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.

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

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

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 `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.

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.

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.QuantizeAndDequantizeV4Grad` 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.LoadAndRemapMatrix 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 `initializing_values` is a vector 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.

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.

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.

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.

NVIDIA DGX Spark GB10 contains a vulnerability in OSROOT firmware, where an attacker could cause an invalid memory read. A successful exploit of this vulnerability might lead to denial of service.

in OpenHarmony v5.0.3 and prior versions allow a local attacker cause DOS through improper input.

in OpenHarmony v5.0.3 and prior versions allow a local attacker case DOS through improper input.

OpenHarmony v3.2.1 and prior version has a system call function usage error. Local attackers can crash kernel by the error input.

Windows Virtual Trusted Platform Module Denial of Service Vulnerability

Windows Virtual Trusted Platform Module Denial of Service Vulnerability