A validation issue was addressed with improved input sanitization. This issue is fixed in iOS 13.5 and iPadOS 13.5, macOS Catalina 10.15.5. A USB device may be able to cause a denial of service.

Improper input validation in Intel(R) Graphics Drivers before version 26.20.100.7212 may allow an authenticated user to enable denial of service via local access.

LaunchServices in Apple OS X before 10.10.3 allows local users to cause a denial of service (Finder crash) via crafted localization data.

The Meeting component in Huawei eSpace Desktop before V100R001C03 allows local users to cause a denial of service (program exit) via a crafted image.

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.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.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, 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.

Multiple memory corruption issues were addressed with improved input validation. This issue is fixed in macOS Mojave 10.14.4. Processing malicious data may lead to unexpected application termination.

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.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.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 `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.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.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.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.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.

arch/x86/kvm/vmx.c in the KVM subsystem in the Linux kernel before 3.12 does not have an exit handler for the INVEPT instruction, which allows guest OS users to cause a denial of service (guest OS crash) via a crafted application.

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.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.

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.

mbae.sys in Malwarebytes Anti-Exploit before 1.05.1.2014 allows local users to cause a denial of service (crash) via a crafted size in an unspecified IOCTL call, which triggers an out-of-bounds read. NOTE: some of these details are obtained from third party information.

Ruby gem openshift-origin-node before 2014-02-14 does not contain a cronjob timeout which could result in a denial of service in cron.daily and cron.weekly.

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, 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.

Insufficient input validation in the firmware for the Intel(R) 700-series of Ethernet Controllers before version 7.3 may allow a privileged user to potentially enable denial of service via local access.

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.

The File Bookmark component in Apple OS X before 10.11.1 allows local users to cause a denial of service (application crash) via crafted bookmark metadata in a folder.

Ghost Security Suite alpha 1.200 does not properly validate certain parameters to System Service Descriptor Table (SSDT) function handlers, which allows local users to cause a denial of service (crash) and possibly gain privileges via the (1) NtCreateKey, (2) NtCreateThread, (3) NtDeleteValueKey, (4) NtQueryValueKey, (5) NtSetSystemInformation, and (6) NtSetValueKey kernel SSDT hooks.

A vulnerability in the bridge protocol data unit (BPDU) forwarding functionality of Cisco Aironet Access Points (APs) could allow an unauthenticated, adjacent attacker to cause an AP port to go into an error disabled state. The vulnerability occurs because BPDUs received from specific wireless clients are forwarded incorrectly. An attacker could exploit this vulnerability on the wireless network by sending a steady stream of crafted BPDU frames. A successful exploit could allow the attacker to cause a limited denial of service (DoS) attack because an AP port could go offline.

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.

Improper input validation in some Intel(R) Optane(TM) PMem versions before versions 1.2.0.5446 or 2.2.0.1547 may allow a privileged user to potentially enable denial of service via local access.

dbus 1.3.0 before 1.6.22 and 1.8.x before 1.8.6, when running on Linux 2.6.37-rc4 or later, allows local users to cause a denial of service (system-bus disconnect of other services or applications) by sending a message containing a file descriptor, then exceeding the maximum recursion depth before the initial message is forwarded.

dbus 1.3.0 before 1.6.22 and 1.8.x before 1.8.6 allows local users to cause a denial of service (disconnect) via a certain sequence of crafted messages that cause the dbus-daemon to forward a message containing an invalid file descriptor.

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

Linux kernel 2.6 and 2.4 on the IA64 architecture allows local users to cause a denial of service (kernel crash) via ptrace and the restore_sigcontext function.

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.

Triangle MicroWorks SCADA Data Gateway before 3.00.0635 allows physically proximate attackers to cause a denial of service (excessive data processing) via a crafted DNP request over a serial line.

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 `CHECK`-fail in `tf.raw_ops.MapStage`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/map_stage_op.cc#L513) does not check that the `key` input is a valid non-empty tensor. We have patched the issue in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. 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.

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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions under certain conditions, Go code can trigger a segfault in string deallocation. For string tensors, `C.TF_TString_Dealloc` is called during garbage collection within a finalizer function. However, tensor structure isn't checked until encoding to avoid a performance penalty. The current method for dealloc assumes that encoding succeeded, but segfaults when a string tensor is garbage collected whose encoding failed (e.g., due to mismatched dimensions). To fix this, the call to set the finalizer function is deferred until `NewTensor` returns and, if encoding failed for a string tensor, deallocs are determined based on bytes written. We have patched the issue in GitHub commit 8721ba96e5760c229217b594f6d2ba332beedf22. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, which is the other affected version.

lnsfw1.sys 6.0.2900.5512 in Look 'n' Stop Firewall 2.06p4 and 2.07 allows local users to cause a denial of service (crash) via a crafted 0x80000064 IOCTL request that triggers an assertion failure. NOTE: some of these details are obtained from third party information.

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.

Apache Karaf before 4.0.10 enables a shutdown port on the loopback interface, which allows local users to cause a denial of service (shutdown) by sending a shutdown command to all listening high ports.