Insufficient input validation in Kernel Mode Driver in Intel(R) Graphics Driver for Windows* before versions 10.18.x.5059 (aka 15.33.x.5059), 10.18.x.5057 (aka 15.36.x.5057), 20.19.x.5063 (aka 15.40.x.5063) 21.20.x.5064 (aka 15.45.x.5064) and 24.20.100.6373 potentially enables a privileged user to cause a denial of service via local access.
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).
Insufficient input validation in Intel(R) Server Platform Services HECI subsystem before version SPS_E5_04.00.04.393.0 may allow privileged user to potentially cause a denial of service via local access.
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.
Insufficient input validation in User Mode Driver in Intel(R) Graphics Driver for Windows* before versions 10.18.x.5059 (aka 15.33.x.5059), 10.18.x.5057 (aka 15.36.x.5057), 20.19.x.5063 (aka 15.40.x.5063) 21.20.x.5064 (aka 15.45.x.5064) and 24.20.100.6373 potentially enables an unprivileged user to cause a 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, 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.
Improper input validation in the Intel(R) SPS versions before SPS_E5_04.04.04.023.0, SPS_E5_04.04.03.228.0 or SPS_SoC-A_05.00.03.098.0 may allow a privileged user to potentially enable denial of service via local access.
An issue was discovered in Alps Pointing-device Driver 10.1.101.207. ApMsgFwd.exe allows the current user to map and write to the "ApMsgFwd File Mapping Object" section. ApMsgFwd.exe uses the data written to this section as arguments to functions. This causes a denial of service condition when invalid pointers are written to the mapped section. This driver has been used with Dell, ThinkPad, and VAIO devices.
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.
Improper input validation 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.
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.
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.
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.
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 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.
The kernel_wait4 function in kernel/exit.c in the Linux kernel before 4.13, when an unspecified architecture and compiler is used, might allow local users to cause a denial of service by triggering an attempted use of the -INT_MIN value.
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.
VMware ESXi (7.0 prior to ESXi70U1c-17325551), VMware Workstation (16.x prior to 16.0 and 15.x prior to 15.5.7), VMware Fusion (12.x prior to 12.0 and 11.x prior to 11.5.7) and VMware Cloud Foundation contain a denial of service vulnerability due to improper input validation in GuestInfo. A malicious actor with normal user privilege access to a virtual machine can crash the virtual machine's vmx process leading to a denial of service condition.
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.
In affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.
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.
Insufficient input validation in the firmware for Intel(R) 722 Ethernet Controllers before version 1.4.3 may allow a privileged user to potentially enable denial of service via local access.
Improper input validation in the Intel(R) SGX Platform Software for Windows* may allow an authenticated user to potentially enable a denial of service via local access.
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.
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.
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.
Philips SureSigns VS4, A.07.107 and prior receives input or data, but it does not validate or incorrectly validates that the input has the properties required to process the data safely and correctly.
An input validation vulnerability exists in Juniper Networks Junos OS, allowing an attacker to crash the srxpfe process, causing a Denial of Service (DoS) through the use of specific maintenance commands. The srxpfe process restarts automatically, but continuous execution of the commands could lead to an extended Denial of Service condition. This issue only affects the SRX1500, SRX4100, SRX4200, NFX150, NFX250, and vSRX-based platforms. No other products or platforms are affected by this vulnerability. This issue affects Juniper Networks Junos OS: 15.1X49 versions prior to 15.1X49-D220 on SRX1500, SRX4100, SRX4200, vSRX; 17.4 versions prior to 17.4R3-S3 on SRX1500, SRX4100, SRX4200, vSRX; 18.1 versions prior to 18.1R3-S11 on SRX1500, SRX4100, SRX4200, vSRX, NFX150; 18.2 versions prior to 18.2R3-S5 on SRX1500, SRX4100, SRX4200, vSRX, NFX150, NFX250; 18.3 versions prior to 18.3R2-S4, 18.3R3-S3 on SRX1500, SRX4100, SRX4200, vSRX, NFX150, NFX250; 18.4 versions prior to 18.4R2-S5, 18.4R3-S4 on SRX1500, SRX4100, SRX4200, vSRX, NFX150, NFX250; 19.1 versions prior to 19.1R3-S2 on SRX1500, SRX4100, SRX4200, vSRX, NFX150, NFX250; 19.2 versions prior to 19.2R1-S5, 19.2R3 on SRX1500, SRX4100, SRX4200, vSRX, NFX150, NFX250. This issue does not affect Junos OS 19.3 or any subsequent version.
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.
The dbus_signature_validate function in the D-bus library (libdbus) before 1.2.4 allows remote attackers to cause a denial of service (application abort) via a message containing a malformed signature, which triggers a failed assertion error.
An Ubuntu-specific modification to AccountsService in versions before 0.6.55-0ubuntu13.2, among other earlier versions, would perform unbounded read operations on user-controlled ~/.pam_environment files, allowing an infinite loop if /dev/zero is symlinked to this location.
Lack of validation on data read from guest memory in IntPeGetDirectory, IntPeParseUnwindData, IntLogExceptionRecord, IntKsymExpandSymbol and IntLixTaskDumpTree may lead to out-of-bounds read or it could cause DoS due to integer-overflor (IntPeGetDirectory), TOCTOU (IntPeParseUnwindData) or insufficient validations.
Memory corruption in IntLixCrashDumpDmesg, IntLixTaskFetchCmdLine, IntLixFileReadDentry and IntLixFileGetPath due to insufficient guest-data input validation may lead to denial of service conditions.
Remote Denial of Service in LwM2M do_write_op_tlv. Zephyr versions >= 1.14.2, >= 2.2.0 contain Improper Input Validation (CWE-20), Loop with Unreachable Exit Condition ('Infinite Loop') (CWE-835). For more information, see https://github.com/zephyrproject-rtos/zephyr/security/advisories/GHSA-g9mg-fj58-6fqh
Improper input validation in some Intel(R) Thunderbolt(TM) controllers may allow an authenticated user to potentially enable denial of service via local access.