PCManFM 1.2.5 insecurely uses /tmp for a socket file, allowing a local user to cause a denial of service (application unavailability).
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.
Insufficient write protection in firmware for Intel(R) Optane(TM) SSD DC P4800X before version E2010435 may allow a privileged user to potentially enable a denial of service via local access.
Improper input validation in some Intel(R) Graphics Drivers for Windows* before version 26.20.100.7212 and before Linux kernel version 5.5 may allow a privileged user to potentially enable a denial of service via local access.
Input validation error in Intel MinnowBoard 3 Firmware versions prior to 0.65 allow local attacker to cause denial of service via UEFI APIs.
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.
Data corruption vulnerability in firmware in Intel Solid-State Drive Consumer, Professional, Embedded, Data Center affected firmware versions LSBG200, LSF031C, LSF036C, LBF010C, LSBG100, LSF031C, LSF036C, LBF010C, LSF031P, LSF036P, LBF010P, LSF031P, LSF036P, LBF010P, LSMG200, LSF031E, LSF036E, LSMG100, LSF031E, LSF036E, LSDG200, LSF031D, LSF036D allows local users to cause a denial of service via unspecified vectors.
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.
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.
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.
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
Linux kernel 2.6.8 to 2.6.14-rc2 allows local users to cause a denial of service (kernel OOPS) via a userspace process that issues a USB Request Block (URB) to a USB device and terminates before the URB is finished, which leads to a stale pointer reference.
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.
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.
AMD Graphics Driver for Windows 10, amdfender.sys may improperly handle input validation on InputBuffer which may result in a denial of service (DoS).
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.
Improper input validation in some Intel(R) Thunderbolt(TM) controllers may allow an authenticated user to potentially enable denial of service via local access.
A Denial Of Service vulnerability exists when Connected User Experiences and Telemetry Service fails to validate certain function values.An attacker who successfully exploited this vulnerability could deny dependent security feature functionality.To exploit this vulnerability, an attacker would have to log on to an affected system and run a specially crafted application.The security update addresses the vulnerability by correcting how the Connected User Experiences and Telemetry Service validates certain function values., aka 'Connected User Experiences and Telemetry Service Denial of Service Vulnerability'. This CVE ID is unique from CVE-2020-1123.
Memory corruption in IntLixCrashDumpDmesg, IntLixTaskFetchCmdLine, IntLixFileReadDentry and IntLixFileGetPath due to insufficient guest-data input validation may lead to denial of service conditions.
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.