A vulnerability in the interprocess communication (IPC) channel of Cisco AnyConnect Secure Mobility Client 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 device. The vulnerability is due to insufficient validation of user-supplied input. An attacker could exploit this vulnerability by sending one or more crafted IPC messages 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. Note: The process under attack will automatically restart so no action is needed by the user or admin.

A flaw was found in NetworkManager in versions before 1.30.0. Setting match.path and activating a profile crashes NetworkManager. The highest threat from this vulnerability is to system availability.

Windows Virtual Trusted Platform Module Denial of Service Vulnerability

Windows Virtual Trusted Platform Module Denial of Service Vulnerability

NVIDIA GPU Display Driver for Windows and Linux, all versions, contains a vulnerability in the kernel mode layer (nvlddmkm.sys) handler for DxgkDdiEscape or IOCTL in which improper validation of a user pointer may lead to denial of service.

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 memory management driver, 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. User interaction is not needed for exploitation. Patch ID: ALPS05403499; Issue ID: ALPS05336700.

In memory management driver, 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. User interaction is not needed for exploitation. Patch ID: ALPS05403499; Issue ID: ALPS05336713.

tuned before 2.x allows local users to kill running processes due to insecure permissions with tuned's ktune service.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the shape inference code for `tf.raw_ops.Dequantize` has a vulnerability that could trigger a denial of service via a segfault if an attacker provides invalid arguments. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/ops/array_ops.cc#L2999-L3014) uses `axis` to select between two different values for `minmax_rank` which is then used to retrieve tensor dimensions. However, code assumes that `axis` can be either `-1` or a value greater than `-1`, with no validation for the other values. We have patched the issue in GitHub commit da857cfa0fde8f79ad0afdbc94e88b5d4bbec764. 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.

Improper Validation of Specified Index, Position, or Offset in Input in firmware for some Intel(R) PROSet/Wireless Wi-Fi in multiple operating systems and some Killer(TM) Wi-Fi in Windows 10 and 11 may allow a privileged user to potentially enable denial of service via local access.

Improper input validation in a subsystem for some Intel Server Boards, Server Systems and Compute Modules before version 1.59 may allow an authenticated user to potentially enable denial of service via local access.

A vulnerability was reported in Lenovo PC Manager versions prior to 2.6.40.3154 that could allow an attacker to cause a system reboot.

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.

Cloudflare version of zlib library was found to be vulnerable to memory corruption issues affecting the deflation algorithm implementation (deflate.c). The issues resulted from improper input validation and heap-based buffer overflow. A local attacker could exploit the problem during compression using a crafted malicious file potentially leading to denial of service of the software. Patches: The issue has been patched in commit 8352d10 https://github.com/cloudflare/zlib/commit/8352d108c05db1bdc5ac3bdf834dad641694c13c . The upstream repository is not affected.

Denial of service (DoS) vulnerability in the installation module Impact: Successful exploitation of this vulnerability will affect availability.

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

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.

Vulnerability of pop-up windows belonging to no app in the VPN module Impact: Successful exploitation of this vulnerability may affect service confidentiality.

Data verification vulnerability in the battery module Impact: Successful exploitation of this vulnerability may affect function stability.

Vulnerability of parameter type not being verified in the WantAgent module Impact: Successful exploitation of this vulnerability may affect availability.

Vulnerability of processes not being fully terminated in the VPN module Impact: Successful exploitation of this vulnerability will affect power consumption.

Vulnerability of input parameters not being verified in the HDC module Impact: Successful exploitation of this vulnerability may affect availability.

The cifs_lookup function in fs/cifs/dir.c in the Linux kernel before 3.2.10 allows local users to cause a denial of service (OOPS) via attempted access to a special file, as demonstrated by a FIFO.

Access permission verification vulnerability in the camera driver module Impact: Successful exploitation of this vulnerability will affect availability.

An improper input validation vulnerability in the UEM Core of BlackBerry UEM version(s) 12.13.0, 12.12.1a QF2 (and earlier), and 12.11.1 QF3 (and earlier) could allow an attacker to potentially cause a Denial of Service (DoS) of the UEM Core service.

IBM AIX 7.2, 7.3, VIOS 3.1, and 4.1 could allow a non-privileged local user to exploit a vulnerability in the AIX perfstat kernel extension to cause a denial of service.

IBM Storage Scale (IBM Spectrum Scale 5.1.0.0 through 5.1.2.9, 5.1.3.0 through 5.1.6.1 and IBM Elastic Storage Systems 6.1.0.0 through 6.1.2.5, 6.1.3.0 through 6.1.6.0) could allow a local user to cause a kernel panic. IBM X-Force ID: 252187.

NVIDIA Jetson Linux Driver Package contains a vulnerability in nvbootctrl, where a privileged local attacker can configure invalid settings, resulting in denial of service.

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.

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.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.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.

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.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.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.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.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.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, 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.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.

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.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.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.

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 `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.

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.