TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues. These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for the `Cudnn*` operations in TensorFlow can be tricked into accessing invalid memory, via a heap buffer overflow. This occurs because the ranks of the `input`, `input_h` and `input_c` parameters are not validated, but code assumes they have certain values. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affected versions an attacker can trigger undefined behavior, integer overflows, segfaults and `CHECK`-fail crashes if they can change saved checkpoints from outside of TensorFlow. This is because the checkpoints loading infrastructure is missing validation for invalid file formats. The fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

TensorFlow is an open source platform for machine learning. In affeced versions during execution, `EinsumHelper::ParseEquation()` is supposed to set the flags in `input_has_ellipsis` vector and `*output_has_ellipsis` boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to `true` and never assigns `false`. This results in unitialized variable access if callers assume that `EinsumHelper::ParseEquation()` always sets these flags. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

In WindowManager, there is a possible way to start a foreground activity from the background due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12LAndroid ID: A-205130886

In copy_from_mbox of sss_ice_util.c, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-202003354References: N/A

In ProtocolStkProactiveCommandAdapter::Init of protocolstkadapter.cpp, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-205035540References: N/A

In NFC, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12LAndroid ID: A-192551247

In gasket_free_coherent_memory_all of gasket_page_table.c, there is a possible memory corruption due to a double free. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-151454974References: N/A

In Bubbles, there is a possible way to interfere with Bubbles due to a permissions bypass. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12LAndroid ID: A-202756848

In ppmp_validate_wsm of drm_fw.c, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-216792660References: N/A

In PermissionController, there is a possible way to delete some local files due to an unsafe PendingIntent. This could lead to local escalation of privilege with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12LAndroid ID: A-194696395

In ProtocolStkProactiveCommandAdapter::Init of protocolstkadapter.cpp, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-205036834References: N/A

In Telephony, there is a possible unauthorized modification of the PLMN SIM file due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12LAndroid ID: A-202760015

In sec_ts_parsing_cmds of (TBD), there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-194499021References: N/A

In TBD of TBD, there is a possible out of bounds write due to memory corruption. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-195726151References: N/A

In PackageManager, there is a possible way to update the last usage time of another package due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12LAndroid ID: A-201534884

In Telecom, there is a possible leak of TTY mode change due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12LAndroid ID: A-203880906

In lwis_top_register_io of lwis_device_top.c, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-205995178References: N/A

In PackageManager, there is a possible way to change the splash screen theme of other apps due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12LAndroid ID: A-206474016

In periodic_io_work_func of lwis_periodic_io.c, there is a possible out of bounds write due to a use after free. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-195607566References: N/A

In the TitanM chip, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-202006191References: N/A

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.BoostedTreesCreateEnsemble` can result in a use after free error if an attacker supplies specially crafted arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent `free`-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed. We have patched the issue in GitHub commit 5ecec9c6fbdbc6be03295685190a45e7eee726ab. 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 read from outside of bounds of heap allocated data by sending invalid arguments to `tf.raw_ops.ResourceScatterUpdate`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L919-L923) has an incomplete validation of the relationship between the shapes of `indices` and `updates`: instead of checking that the shape of `indices` is a prefix of the shape of `updates` (so that broadcasting can happen), code only checks that the number of elements in these two tensors are in a divisibility relationship. We have patched the issue in GitHub commit 01cff3f986259d661103412a20745928c727326f. 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 cause undefined behavior via binding a reference to null pointer in all binary cwise operations that don't require broadcasting (e.g., gradients of binary cwise operations). The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/cwise_ops_common.h#L264) assumes that the two inputs have exactly the same number of elements but does not check that. Hence, when the eigen functor executes it triggers heap OOB reads and undefined behavior due to binding to nullptr. We have patched the issue in GitHub commit 93f428fd1768df147171ed674fee1fc5ab8309ec. 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.

In the Android kernel in the kernel MMU code there is a possible execution path leaving some kernel text and rodata pages writable. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.ExperimentalDatasetToTFRecord` and `tf.raw_ops.DatasetToTFRecord` can trigger heap buffer overflow and segmentation fault. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc#L93-L102) assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types. We have patched the issue in GitHub commit e0b6e58c328059829c3eb968136f17aa72b6c876. 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 it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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 cause undefined behavior via binding a reference to null pointer in all operations of type `tf.raw_ops.MatrixDiagV*`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of `k` is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. We have patched the issue in GitHub commit f2a673bd34f0d64b8e40a551ac78989d16daad09. 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 generate undefined behavior via a reference binding to nullptr in `BoostedTreesCalculateBestGainsPerFeature` and similar attack can occur in `BoostedTreesCalculateBestFeatureSplitV2`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/stats_ops.cc) does not validate the input values. We have patched the issue in GitHub commit 9c87c32c710d0b5b53dc6fd3bfde4046e1f7a5ad and in commit 429f009d2b2c09028647dd4bb7b3f6f414bbaad7. 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 the code for `tf.raw_ops.SaveV2` does not properly validate the inputs and an attacker can trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/save_restore_v2_ops.cc) uses `ValidateInputs` to check that the input arguments are valid. This validation would have caught the illegal state represented by the reproducer above. However, the validation uses `OP_REQUIRES` which translates to setting the `Status` object of the current `OpKernelContext` to an error status, followed by an empty `return` statement which just terminates the execution of the function it is present in. However, this does not mean that the kernel execution is finalized: instead, execution continues from the next line in `Compute` that follows the call to `ValidateInputs`. This is equivalent to lacking the validation. We have patched the issue in GitHub commit 9728c60e136912a12d99ca56e106b7cce7af5986. 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 the implementation for `tf.raw_ops.FractionalAvgPoolGrad` can be tricked into accessing data outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/fractional_avg_pool_op.cc#L205) does not validate that the input tensor is non-empty. Thus, code constructs an empty `EigenDoubleMatrixMap` and then accesses this buffer with indices that are outside of the empty area. We have patched the issue in GitHub commit 0f931751fb20f565c4e94aa6df58d54a003cdb30. 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.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, potential buffer overflow can happen when processing AOA measurement event from WIGIG firmware in wil_aoa_evt_meas().

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all operations of type `tf.raw_ops.MatrixSetDiagV*`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of `k` is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. We have patched the issue in GitHub commit ff8894044dfae5568ecbf2ed514c1a37dc394f1b. 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.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, improper input validation for wmi_event->num_vdev_mac_entries in wma_pdev_set_hw_mode_resp_evt_handler(), which is received from firmware, leads to potential buffer overflow.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the `input` tensor. A similar issue occurs in `MklRequantizePerChannelOp`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments. We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9. 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.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, improper input validation for vent->vdev_id in wma_action_frame_filter_mac_event_handler(), which is received from firmware, leads to arbitrary code execution.

In msm_isp_prepare_v4l2_buf in Android for MSM, Firefox OS for MSM, and QRD Android before 2017-02-12, an array out of bounds can occur.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, improper controls in MSM CORE leads to use memory after it is freed in msm_core_ioctl().

Improper access control vulnerability in dynamic receiver in ApkInstaller prior to SMR MAR-2022 Release allows unauthorized attackers to execute arbitrary activity without a proper permission

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of SVDF in TFLite is [vulnerable to a null pointer error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/svdf.cc#L300-L313). The [`GetVariableInput` function](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L115-L119) can return a null pointer but `GetTensorData` assumes that the argument is always a valid tensor. Furthermore, because `GetVariableInput` calls [`GetMutableInput`](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/kernel_util.cc#L82-L90) which might return `nullptr`, the `tensor->is_variable` expression can also trigger a null pointer exception. We have patched the issue in GitHub commit 5b048e87e4e55990dae6b547add4dae59f4e1c76. 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 cause undefined behavior via binding a reference to null pointer in `tf.raw_ops.RaggedTensorToVariant`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L129) has an incomplete validation of the splits values, missing the case when the argument would be empty. We have patched the issue in GitHub commit be7a4de6adfbd303ce08be4332554dff70362612. 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. Sending invalid argument for `row_partition_types` of `tf.raw_ops.RaggedTensorToTensor` API results in a null pointer dereference and undefined behavior. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L328) accesses the first element of a user supplied list of values without validating that the provided list is not empty. We have patched the issue in GitHub commit 301ae88b331d37a2a16159b65b255f4f9eb39314. 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.

An issue was discovered on LG mobile devices with Android OS 9.0 software. The HAL service has a buffer overflow that leads to arbitrary code execution. The LG ID is LVE-SMP-190013 (September 2019).

In apusys, there is a possible out of bounds write due to a stack-based buffer overflow. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05672103; Issue ID: ALPS05672103.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, potential buffer overflow can happen when processing UTF event in wma_process_utf_event().

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, improper input validation for num_vdev_mac_entries in wma_pdev_hw_mode_transition_evt_handler(), which is received from firmware, leads to potential buffer overflow.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, an invalid input of firmware size (negative value) from user space can potentially lead to the memory leak or buffer overflow during the WLAN cal data store operation.

In Qualcomm Android for MSM, Firefox OS for MSM, and QRD Android with all Android releases from CAF using the Linux kernel before security patch level 2018-04-05, the camera application triggers "user-memory-access" issue as the Camera CPP module Linux driver directly accesses the application provided buffer, which resides in user space. An unchecked userspace value (ioctl_ptr->len) is used to copy contents to a kernel buffer which can lead to kernel buffer overflow.