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.
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.
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.
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).
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.
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.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.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.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.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 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.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.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.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.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.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.
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.
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.
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.
Improper input validation in some Intel(R) XMM(TM) 7560 Modem software before version M2_7560_R_01.2146.00 may allow a privileged user to potentially enable escalation of privilege via local access.
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.
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.
Improper input validation in the Intel(R) VROC software before version 7.7.6.1003 may allow an authenticated user to potentially enable denial of service via local access.
A vulnerability in the improper handling of junctions before deletion in Bitdefender Total Security 2020 can allow an attacker to to trigger a denial of service on the affected device.
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, 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.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 `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, 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 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.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.
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.
In video decoder, there is a possible improper input validation. This could lead to local denial of service with no additional execution privileges needed
OpenHarmony v3.2.1 and prior version has a system call function usage error. Local attackers can crash kernel by the error input.
Improper input validation for some Intel(R) Processors may allow an authenticated user to potentially cause a denial of service via local access.
Improper input validation in Intel(R) Media SDK software all versions may allow an authenticated user to potentially enable denial of service via local access.
Improper input validation in AMD Crash Defender could allow an attacker to provide the Windows® system process ID to a kernel-mode driver, resulting in an operating system crash, potentially leading to denial of service.
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.
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 drop detection 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.
A vulnerability in Cisco AppDynamics Network Visibility Agent could allow an unauthenticated, local attacker to cause a denial of service (DoS) condition on an affected device. This vulnerability is due to the inability to handle unexpected input. An attacker who has local device access could exploit this vulnerability by sending an HTTP request to the targeted service. A successful exploit could allow the attacker to cause a DoS condition by stopping the Network Agent Service on the local device.
A vulnerability in the Tool Command Language (Tcl) interpreter of Cisco IOS Software and Cisco IOS XE Software could allow an authenticated, local attacker with privileged EXEC credentials to cause a denial of service (DoS) condition on an affected system. The vulnerability is due to insufficient input validation of data passed to the Tcl interpreter. An attacker could exploit this vulnerability by executing crafted Tcl arguments on an affected device. An exploit could allow the attacker to cause the affected device to reload, resulting in a DoS condition.
An IBM UrbanCode Deploy Agent 7.2 through 7.2.3.7, and 7.3 through 7.3.2.2 installed as a Windows service in a non-standard location could be subject to a denial of service attack by local accounts. IBM X-Force ID: 265509.
Issue of buffer overflow caused by insufficient data verification in the kernel gyroscope module. Impact: Successful exploitation of this vulnerability may affect availability.
Improper input validation in the Intel(R) Data Center Manager software before version 4.1 may allow an authenticated user to potentially enable denial of service via local access.
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.
IBM Common Cryptographic Architecture (CCA 5.x MTM for 4767 and CCA 7.x MTM for 4769) could allow a local user to cause a denial of service due to improper input validation. IBM X-Force ID: 223596.
Improper input validation in some Intel(R) Ethernet E810 Adapter drivers for Linux before version 1.0.4 and before version 1.4.29.0 for Windows*, may allow an authenticated user to potentially enable a denial of service via local access.
Improper input validation in the firmware for some Intel(R) Processors may allow an authenticated user to potentially enable denial of service via local access.