Windows Virtual Trusted Platform Module Denial of Service Vulnerability
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.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.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.
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.
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.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.
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.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.
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.
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
Vulnerability of input parameters not being verified in the HDC module Impact: Successful exploitation of this vulnerability may affect availability.
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.
Improper input validation in AMD μProf could allow an attacker to perform a write to an invalid address, potentially resulting in denial of service.
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.
IBM AIX 7.2, 7.3, VIOS 3.1, and 4.1 could allow a non-privileged local user to exploit a vulnerability in the AIX perfstat kernel extension to cause a denial of service.
An exploitable privilege escalation vulnerability exists in the Shimo VPN 4.1.5.1 helper service in the disconnectService functionality. A non-root user is able to kill any privileged process on the system. An attacker would need local access to the machine for a successful exploit.
An exploitable denial-of-service vulnerability exists in the helper service of Clean My Mac X, version 4.04, due to improper input validation. A user with local access can use this vulnerability to terminate a privileged helper application. An attacker would need local access to the machine for a successful exploit.
in OpenHarmony v4.1.0 and prior versions allow a local attacker cause DOS through improper input.
An exploitable local denial-of-service vulnerability exists in the privileged helper tool of GOG Galaxy's Games, version 1.2.47 for macOS. An attacker can send malicious data to the root-listening service, causing the application to terminate and become unavailable.
Parameter corruption in NDIS filter driver in Intel Online Connect Access 1.9.22.0 allows an attacker to cause a denial of service via local access.
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 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 for some Intel(R) Processors may allow an authenticated user to potentially cause a denial of service via local access.
In get of PacProxyService.java, there is a possible system service crash due to improper input validation. This could lead to local denial of service with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10 Android-11 Android-12 Android-12LAndroid ID: A-219498290
Improper input validation in the Intel(R) Ethernet ixgbe driver for Linux before version 3.17.3 may allow an authenticated user to potentially enable denial of service via local access.
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.
A vulnerability was reported in Lenovo PC Manager versions prior to 2.6.40.3154 that could allow an attacker to cause a system reboot.
In registerPhoneAccount of PhoneAccountRegistrar.java, there is a possible way to prevent the user from selecting a phone account 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.Product: AndroidVersions: Android-10 Android-11 Android-12 Android-12LAndroid ID: A-217934478
TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseReshape` results in a denial of service based on a `CHECK`-failure. The implementation(https://github.com/tensorflow/tensorflow/blob/e87b51ce05c3eb172065a6ea5f48415854223285/tensorflow/core/kernels/sparse_reshape_op.cc#L40) has no validation that the input arguments specify a valid sparse tensor. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are the only affected versions.
An improper input validation vulnerability in loading graph file in DSP driver prior to SMR Sep-2021 Release 1 allows attackers to perform permanent denial of service on the device.
Denial of service (DoS) vulnerability in the installation module Impact: Successful exploitation of this vulnerability will affect availability.
Vulnerability of parameter type not being verified in the WantAgent module Impact: Successful exploitation of this vulnerability may affect availability.
Vulnerability of pop-up windows belonging to no app in the VPN module Impact: Successful exploitation of this vulnerability may affect service confidentiality.
Vulnerability of parameter type not being verified in the WantAgent module Impact: Successful exploitation of this vulnerability may affect availability.
Vulnerability of input parameters not being verified in the HDC module Impact: Successful exploitation of this vulnerability may affect availability.
Data verification vulnerability in the battery module Impact: Successful exploitation of this vulnerability may affect function stability.
Transient Denial-of-service in Automotive due to improper input validation while parsing ELF file.
Vulnerability of processes not being fully terminated in the VPN module Impact: Successful exploitation of this vulnerability will affect power consumption.
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 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.
Access permission verification vulnerability in the camera driver module Impact: Successful exploitation of this vulnerability will affect availability.
Insufficient bound checks in the System Management Unit (SMU) may result in a system voltage malfunction that could result in denial of resources and/or possibly denial of service.
Insufficient DRAM address validation in System Management Unit (SMU) may result in a DMA (Direct Memory Access) read/write from/to invalid DRAM address that could result in denial of service.
Insufficient input validation in the SNP_GUEST_REQUEST command may lead to a potential data abort error and a denial of service.
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: ALPS05336706.
A vulnerability in the interprocess communication (IPC) channel of Cisco AnyConnect Secure Mobility Client 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 device. The vulnerability is due to insufficient validation of user-supplied input. An attacker could exploit this vulnerability by sending one or more crafted IPC messages 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. Note: The process under attack will automatically restart so no action is needed by the user or admin.