In multiple functions of DevicePolicyManager.java, there is a possible way to prevent enabling the Find my Device feature 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.

In several functions of PhoneAccountRegistrar.java, there is a possible way to prevent an access to emergency services 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-11 Android-12 Android-12L Android-13Android ID: A-256819769

In multiple functions of JobStore.java, there is a possible way to cause a crash on startup 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-11 Android-12 Android-12L Android-13Android ID: A-246542285

In multiple functions of multiple files, there is a possible way to make the device unusable 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-11 Android-12 Android-12L Android-13Android ID: A-268193777

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.

In telephony service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.

In TeleService, 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

In dialer service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.

In telephony service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.

In soter service, there is a possible missing permission check. This could lead to local denial of service with no additional execution privileges.

In vdsp service, there is a missing permission check. This could lead to local denial of service in vdsp service.

In telecom service, there is a missing permission check. This could lead to local denial of service in telecom service.

In log service, there is a missing permission check. This could lead to local denial of service in log service.

In telecom service, there is a missing permission check. This could lead to local denial of service in telecom service.

In log service, there is a missing permission check. This could lead to local denial of service in log service.

TensorFlow is an end-to-end open source platform for machine learning. The code for `tf.raw_ops.UncompressElement` can be made to trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/compression_ops.cc#L50-L53) obtains a pointer to a `CompressedElement` from a `Variant` tensor and then proceeds to dereference it for decompressing. There is no check that the `Variant` tensor contained a `CompressedElement`, so the pointer is actually `nullptr`. We have patched the issue in GitHub commit 7bdf50bb4f5c54a4997c379092888546c97c3ebd. 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.

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 craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. This is caused by the MLIR optimization of `L2NormalizeReduceAxis` operator. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. We have patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955. 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.

The updateMessageStatus function in Android 5.1.1 and earlier allows local users to cause a denial of service (NULL pointer exception and process crash).

In video decoder, there is a possible improper input validation. This could lead to local denial of service with no additional execution privileges needed

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.SdcaOptimizer` triggers undefined behavior due to dereferencing a null pointer. The implementation(https://github.com/tensorflow/tensorflow/blob/60a45c8b6192a4699f2e2709a2645a751d435cc3/tensorflow/core/kernels/sdca_internal.cc) does not validate that the user supplied arguments satisfy all constraints expected by the op(https://www.tensorflow.org/api_docs/python/tf/raw_ops/SdcaOptimizer). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a null pointer dereference in the implementation of `tf.raw_ops.EditDistance`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/79865b542f9ffdc9caeb255631f7c56f1d4b6517/tensorflow/core/kernels/edit_distance_op.cc#L103-L159) has incomplete validation of the input parameters. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

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 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.

TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.RaggedTensorToVariant` with arguments specifying an invalid ragged tensor results in a null pointer dereference. The implementation of `RaggedTensorToVariant` operations(https://github.com/tensorflow/tensorflow/blob/904b3926ed1c6c70380d5313d282d248a776baa1/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L39-L40) does not validate that the ragged tensor argument is non-empty. Since `batched_ragged` contains no elements, `batched_ragged.splits` is a null vector, thus `batched_ragged.splits(0)` will result in dereferencing `nullptr`. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

NULL pointer dereference vulnerability in ION driver prior to SMR Sep-2021 Release 1 allows attackers to cause memory corruption.

Improper address validation in HArx in Samsung mobile devices prior to SMR Mar-2021 Release 1 allows an attacker, given a compromised kernel, to corrupt EL2 memory.

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.

A vulnerability in mfc driver prior to SMR Oct-2021 Release 1 allows memory corruption via NULL-pointer dereference.

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: ALPS05336700.

TensorFlow is an open source platform for machine learning. If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a `nullptr`, which is not caught. An example can be seen in `tf.compat.v1.extract_volume_patches` by passing in quantized tensors as input `ksizes`. We have patched the issue in GitHub commit e9e95553e5411834d215e6770c81a83a3d0866ce. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

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: ALPS05336713.

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 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.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.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 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 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.

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.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.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.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.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.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.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.