A denial of service vulnerability exists when Microsoft Hyper-V on a host server fails to properly validate specific malicious data from a user on a guest operating system.To exploit the vulnerability, an attacker who already has a privileged account on a guest operating system, running as a virtual machine, could run a specially crafted application.The security update addresses the vulnerability by resolving the conditions where Hyper-V would fail to handle these requests., aka 'Windows Hyper-V Denial of Service Vulnerability'. This CVE ID is unique from CVE-2020-0661.

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.

In the settings app, there is a possible app crash due to improper input validation. This could lead to local denial of service of the Settings app with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10Android ID: A-136005061

Remote Desktop in Windows XP SP1 does not verify the "Force shutdown from a remote system" setting, which allows remote attackers to shut down the system by executing TSShutdn.exe.

Certain NETGEAR devices are affected by denial of service. This affects M4300-28G before 12.0.2.15, M4300-52G before 12.0.2.15, M4300-28G-POE+ before 12.0.2.15, M4300-52G-POE+ before 12.0.2.15, M4300-8X8F before 12.0.2.15, M4300-12X12F before 12.0.2.15, M4300-24X24F before 12.0.2.15, M4300-24X before 12.0.2.15, M4300-48X before 12.0.2.15, and M4200 before 12.0.2.15.

PEM module of DP300 V500R002C00; IPS Module V500R001C00; V500R001C30; NGFW Module V500R001C00; V500R002C00; NIP6300 V500R001C00; V500R001C30; NIP6600 V500R001C00; V500R001C30; RP200 V500R002C00; V600R006C00; S12700 V200R007C00; V200R007C01; V200R008C00; V200R009C00; V200R010C00; S1700 V200R006C10; V200R009C00; V200R010C00; S2700 V200R006C10; V200R007C00; V200R008C00; V200R009C00; V200R010C00; S5700 V200R006C00; V200R007C00; V200R008C00; V200R009C00; V200R010C00; S6700 V200R008C00; V200R009C00; V200R010C00; S7700 V200R007C00; V200R008C00; V200R009C00; V200R010C00; S9700 V200R007C00; V200R007C01; V200R008C00; V200R009C00; V200R010C00; Secospace USG6300 V500R001C00; V500R001C30; Secospace USG6500 V500R001C00; V500R001C30; Secospace USG6600 V500R001C00; V500R001C30S; TE30 V100R001C02; V100R001C10; V500R002C00; V600R006C00; TE40 V500R002C00; V600R006C00; TE50 V500R002C00; V600R006C00; TE60 V100R001C01; V100R001C10; V500R002C00; V600R006C00; TP3106 V100R002C00; TP3206 V100R002C00; V100R002C10; USG9500 V500R001C00; V500R001C30; ViewPoint 9030 V100R011C02; V100R011C03 has a DoS vulnerability in PEM module of Huawei products due to insufficient verification. An authenticated local attacker can make processing into deadloop by a malicious certificate. The attacker can exploit this vulnerability to cause a denial of service.

Huawei AR120-S V200R006C10, V200R007C00, V200R008C20, V200R008C30, AR1200 V200R006C10, V200R006C13, V200R007C00, V200R007C01, V200R007C02, V200R008C20, V200R008C30, AR1200-S V200R006C10, V200R007C00, V200R008C20, V200R008C30, AR150 V200R006C10, V200R007C00, V200R007C01, V200R007C02, V200R008C20, V200R008C30, AR150-S V200R006C10, V200R007C00, V200R008C20, V200R008C30, AR160 V200R006C10, V200R006C12, V200R007C00, V200R007C01, V200R007C02, V200R008C20, V200R008C30, AR200 V200R006C10, V200R007C00, V200R007C01, V200R008C20, V200R008C30, AR200-S V200R006C10, V200R007C00, V200R008C20, V200R008C30, AR2200 V200R006C10, V200R006C13, V200R006C16, V200R007C00, V200R007C01, V200R007C02, V200R008C20, V200R008C30, AR2200-S V200R006C10, V200R007C00, V200R008C20, V200R008C30, AR3200 V200R006C10, V200R006C11, V200R007C00, V200R007C01, V200R007C02, V200R008C00, V200R008C10, V200R008C20, V200R008C30, AR3600 V200R006C10, V200R007C00, V200R007C01, V200R008C20, AR510 V200R006C10, V200R006C12, V200R006C13, V200R006C15, V200R006C16, V200R006C17, V200R007C00, V200R008C20, V200R008C30, DP300 V500R002C00, MAX PRESENCE V100R001C00, NetEngine16EX V200R006C10, V200R007C00, V200R008C20, V200R008C30, RP200 V500R002C00, V600R006C00, SRG1300 V200R006C10, V200R007C00, V200R007C02, V200R008C20, V200R008C30, SRG2300 V200R006C10, V200R007C00, V200R007C02, V200R008C20, V200R008C30, SRG3300 V200R006C10, V200R007C00, V200R008C20, V200R008C30, TE30 V100R001C02, V100R001C10, V500R002C00, V600R006C00, TE40 V500R002C00, V600R006C00, TE50 V500R002C00, V600R006C00, TE60 V100R001C01, V100R001C10, V500R002C00, V600R006C00, TP3106 V100R002C00, TP3206 V100R002C00, V100R002C10 have a denial of service vulnerability in the specific module. An authenticated, local attacker may craft a specific XML file to the affected products. Due to improper handling of input, successful exploit will cause some service abnormal.

In Vectura Perfect Privacy VPN Manager v1.10.10 and v1.10.11, when resetting the network data via the software client, with a running VPN connection, a critical error occurs which leads to a "FrmAdvancedProtection" crash. Although the mechanism malfunctions and an error occurs during the runtime with the stack trace being issued, the software process is not properly terminated. The software client is still attempting to maintain the connection even though the network connection information is being reset live. In that insecure mode, the "FrmAdvancedProtection" component crashes, but the process continues to run with different errors and process corruptions. This local corruption vulnerability can be exploited by local attackers.

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, 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.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.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.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.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.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.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.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, 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.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.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.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.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.

The KVM subsystem in the Linux kernel through 4.13.3 allows guest OS users to cause a denial of service (assertion failure, and hypervisor hang or crash) via an out-of bounds guest_irq value, related to arch/x86/kvm/vmx.c and virt/kvm/eventfd.c.

The tcmu-runner daemon in tcmu-runner version 1.0.5 to 1.2.0 is vulnerable to a local denial of service attack

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.

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 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 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.

docker2aci <= 0.12.3 has an infinite loop when handling local images with cyclic dependency chain.

<p>A denial of service vulnerability exists when Microsoft Hyper-V on a host server fails to properly validate specific malicious data from a user on a guest operating system.</p> <p>To exploit the vulnerability, an attacker who already has a privileged account on a guest operating system, running as a virtual machine, could run a specially crafted application.</p> <p>The security update addresses the vulnerability by resolving the conditions where Hyper-V would fail to handle these requests.</p>

A vulnerability has been identified in SIMATIC RTLS Locating Manager (All versions < V2.12). The affected application does not properly handle the import of large configuration files. A local attacker could import a specially crafted file which could lead to a denial-of-service condition of the application service.

The imx_fec_do_tx function in hw/net/imx_fec.c in QEMU (aka Quick Emulator) does not properly limit the buffer descriptor count when transmitting packets, which allows local guest OS administrators to cause a denial of service (infinite loop and QEMU process crash) via vectors involving a buffer descriptor with a length of 0 and crafted values in bd.flags.

Application Firewall in Apple OS X before 10.12 allows local users to cause a denial of service via vectors involving a crafted SO_EXECPATH environment variable.

Samsung devices with Android KK(4.4) or L(5.0/5.1) allow local users to cause a denial of service (IAndroidShm service crash) via crafted data in a service call.

A local denial of service vulnerability exists in window broadcast message handling functionality of Kaspersky Anti-Virus software. Sending certain unhandled window messages, an attacker can cause application termination and in the same way bypass KAV self-protection mechanism.

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.

Xen and the Linux kernel through 4.5.x do not properly suppress hugetlbfs support in x86 PV guests, which allows local PV guest OS users to cause a denial of service (guest OS crash) by attempting to access a hugetlbfs mapped area.

The PV domain builder in Xen 4.2 and earlier does not validate the size of the kernel or ramdisk (1) before or (2) after decompression, which allows local guest administrators to cause a denial of service (domain 0 memory consumption) via a crafted (a) kernel or (b) ramdisk.

The cleanup_journal_tail function in the Journaling Block Device (JBD) functionality in the Linux kernel 2.6 allows local users to cause a denial of service (assertion error and kernel oops) via an ext3 or ext4 image with an "invalid log first block value."

acpid.c in acpid before 2.0.9 does not properly handle a situation in which a process has connected to acpid.socket but is not reading any data, which allows local users to cause a denial of service (daemon hang) via a crafted application that performs a connect system call but no read system calls.

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.

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.

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.

The staprun runtime tool in SystemTap 1.3 does not verify that a module to unload was previously loaded by SystemTap, which allows local users to cause a denial of service (unloading of arbitrary kernel modules).

The pipe_fcntl function in fs/pipe.c in the Linux kernel before 2.6.37 does not properly determine whether a file is a named pipe, which allows local users to cause a denial of service via an F_SETPIPE_SZ fcntl call.

Insufficient Input validation in the subsystem for Intel(R) CSME before versions 12.0.45,13.0.10 and 14.0.10 may allow a privileged user to potentially enable denial of service via local access.

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.