TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully. Since the constructor(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values. 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. The implementation of `tf.raw_ops.AvgPool3DGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. 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.

In all Android releases from CAF using the Linux kernel, while processing a voice SVC request which is nonstandard by specifying a payload size that will overflow its own declared size, an out of bounds memory copy occurs.

Invalid memory access in Sentencepiece versions less than 0.2.1 when using a vulnerable model file, which is not created in the normal training procedure.

In Bitmap_createFromParcel of Bitmap.cpp, there is a possible arbitrary code execution due to a missing bounds check. This could lead to local escalation of privilege with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12 Android-12LAndroid ID: A-213169612

In onUidStateChanged of AppOpsService.java, there is a possible way to access location without a visible indicator due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12Android ID: A-208662370

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/ab1e644b48c82cb71493f4362b4dd38f4577a1cf/tensorflow/core/kernels/maxpooling_op.cc#L194-L203) fails to validate that indices used to access elements of input/output arrays are valid. Whereas accesses to `input_backprop_flat` are guarded by `FastBoundsCheck`, the indexing in `out_backprop_flat` can result in OOB access. 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. The implementation of `tf.raw_ops.FractionalAvgPoolGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/dcba796a28364d6d7f003f6fe733d82726dda713/tensorflow/core/kernels/fractional_avg_pool_op.cc#L216) fails to validate that the pooling sequence arguments have enough elements as required by the `out_backprop` tensor shape. 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.

An arbitrary memory overwrite vulnerability in Asylo versions up to 0.6.0 allow an attacker to make an Ecall_restore function call to reallocate untrusted code and overwrite sections of the Enclave memory address. We recommend updating your library.

In CryptoPlugin::decrypt of CryptoPlugin.cpp, there is a possible out of bounds write due to stale pointer. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-8.0 Android-8.1 Android-9 Android-10Android ID: A-144351324

In ppmp_unprotect_buf of drm_fw.c, there is a possible compromise of protected memory due to a logic error in the code. This could lead to local escalation of privilege to TEE with no additional execution privileges needed. User interaction is not needed for exploitation.

NVIDIA Shield TV Experience prior to v8.0.1, NVIDIA Tegra bootloader contains a vulnerability where the software performs an incorrect bounds check, which may lead to buffer overflow resulting in escalation of privileges and code execution. escalation of privileges, and information disclosure, code execution, denial of service, or escalation of privileges.

In writeInplace of Parcel.cpp, there is a possible out of bounds write. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In multiple locations, there is a possible way to reveal images across users due to improper input validation. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is needed for exploitation.

In checkKeyIntentParceledCorrectly of AccountManagerService.java, there is a possible way to bypass parcel mismatch mitigation due to unsafe deserialization. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is needed for exploitation.

In multiple locations, there is a possible way to leak audio files across user profiles due to a confused deputy. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In many functions of ComputerEngine.java, there is a possible way to access URIs across users due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In FrpBypassAlertActivity of FrpBypassAlertActivity.java, there is a possible way to bypass FRP 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.

In multiple functions of Permissions.java, there is a possible way to override the state of the user's location permissions due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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

In multiple functions of CompanionDeviceManagerService.java, there is a possible way to grant permissions without user consent 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.

In applyTaskFragmentOperation of WindowOrganizerController.java, there is a possible way to launch arbitrary activities as the system UID due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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

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

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

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

In FuseDaemon.cpp, there is a possible out of bounds write due to memory corruption. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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

In avrc_vendor_msg of avrc_opt.cc, there is a possible out of bounds write due to a heap buffer overflow. This could lead to paired device escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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

NVIDIA Linux kernel distributions contain a vulnerability in nvmap NVGPU_IOCTL_CHANNEL_SET_ERROR_NOTIFIER, where improper access control may lead to code execution, compromised integrity, or denial of service.

In addWindow of WindowManagerService.java, there is a possible tapjacking issue due to a tapjacking/overlay attack. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

Intent redirection in Samsung Experience Service versions 10.8.0.4 in Android P(9.0) below, and 12.2.0.5 in Android Q(10.0) above allows attacker to execute privileged action.

Improper input validation for some Intel Unison software may allow an authenticated user to potentially enable escalation of privilege via local access.

Using unsafe PendingIntent in Customization Service prior to version 2.2.02.1 in Android O(8.x), 2.4.03.0 in Android P(9.0), 2.7.02.1 in Android Q(10.0) and 2.9.01.1 in Android R(11.0) allows local attackers to perform unauthorized action without permission via hijacking the PendingIntent.

An improper length check in APAService prior to SMR Sep-2021 Release 1 results in stack based Buffer Overflow.

An improper exception control in softsimd prior to SMR APR-2021 Release 1 allows unprivileged applications to access the API in softsimd.

An improper validation vulnerability in FilterProvider prior to SMR Dec-2021 Release 1 allows attackers to write arbitrary files via a path traversal vulnerability.

An improper validation vulnerability in FilterProvider prior to SMR Dec-2021 Release 1 allows local arbitrary code execution.

Improper validation check vulnerability in PackageManager prior to SMR July-2021 Release 1 allows untrusted applications to get dangerous level permission without user confirmation in limited circumstances.

Using unsafe PendingIntent in Samsung Account in versions 10.8.0.4 in Android P(9.0) and below, and 12.1.1.3 in Android Q(10.0) and above allows local attackers to perform unauthorized action without permission via hijacking the PendingIntent.

A possible out of bounds write vulnerability in NPU driver prior to SMR JUN-2021 Release 1 allows arbitrary memory write.

An improper input validation vulnerability in LDFW prior to SMR Dec-2021 Release 1 allows attackers to perform arbitrary code execution.

Improper sanitization of incoming intent in Samsung Contacts prior to SMR JUN-2021 Release 1 allows local attackers to copy or overwrite arbitrary files with Samsung Contacts privilege.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices. 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. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L446). Before the `for` loop, `batch_idx` is set to 0. The attacker sets `splits(0)` to be 7, hence the `while` loop does not execute and `batch_idx` remains 0. This then results in writing to `out(-1, bin)`, which is before the heap allocated buffer for the output 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 also affected.

TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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.

A possible buffer overflow vulnerability in NPU driver prior to SMR JUN-2021 Release 1 allows arbitrary memory write and code execution.

Using unsafe PendingIntent in Slow Motion Editor prior to version 3.5.18.5 allows local attackers unauthorized action without permission via hijacking the PendingIntent.

TensorFlow is an end-to-end open source platform for machine learning. The optimized implementation of the `TransposeConv` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L5221-L5222). An attacker can craft a model such that `stride_{h,w}` values are 0. Code calling this function must validate these arguments. 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.