An issue was discovered on Samsung mobile devices with P(9.0) (Exynos chipsets) software. The S.LSI Wi-Fi drivers have a buffer overflow. The Samsung ID is SVE-2020-16906 (May 2020).

In multiple functions, there is a possible desync in persistence due to an incorrect bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

An improper boundary check in eden_runtime hal service prior to SMR Feb-2022 Release 1 allows arbitrary memory write and code execution.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference function for `Transpose` is vulnerable to a heap buffer overflow. This occurs whenever `perm` contains negative elements. The shape inference function does not validate that the indices in `perm` are all valid. 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 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.

In construct_transaction_from_cmd of lwis_ioctl.c, there is a possible out of bounds write due to a heap buffer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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).

TensorFlow is an end-to-end open source platform for machine learning. Missing validation between arguments to `tf.raw_ops.Conv3DBackprop*` operations can result in heap buffer overflows. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/4814fafb0ca6b5ab58a09411523b2193fed23fed/tensorflow/core/kernels/conv_grad_shape_utils.cc#L94-L153) assumes that the `input`, `filter_sizes` and `out_backprop` tensors have the same shape, as they are accessed in parallel. 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 heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`. The implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty. Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen in this op's implementation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. 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 cause a heap buffer overflow to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor. 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 the MTK_FLP_MSG_HAL_DIAG_REPORT_DATA_NTF handler of flp2hal_- interface.c, there is a possible stack buffer overflow due to a missing bounds check. This could lead to local escalation of privilege in a privileged process with System execution privileges needed. User interaction is not needed for exploitation.

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

In multiple functions of gl_proc.c, there is a buffer overwrite due to a missing bounds check. This could lead to escalation of privileges in the kernel.

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-L433). Before the `for` loop, `batch_idx` is set to 0. The user controls the `splits` array, making it contain only one element, 0. Thus, the code in the `while` loop would increment `batch_idx` and then try to read `splits(1)`, which is outside of bounds. 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.

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

In IoT Devices SDK, there is an implementation of calloc() that doesn't have a length check. An attacker could pass in memory objects larger than the buffer and wrap around to have a smaller buffer than required, allowing the attacker access to the other parts of the heap. We recommend upgrading the Google Cloud IoT Device SDK for Embedded C used to 1.0.3 or greater.

In DeviceId of DeviceId.java, there is a possible desync in persistence due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In GetTachyonCommand of tachyon_server_common.h, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In GetHostAddress of gxp_buffer.h, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In GetHostAddress of gxp_buffer.h, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In GetHostAddress of gxp_buffer.h, there is a possible out of bounds write due to an incorrect bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In onCreate of NotificationAccessSettings.java, there is a possible failure to persist notifications settings due to improper input validation. This could lead to local escalation of privilege 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-260570119

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.

An issue was discovered on Samsung mobile devices with Q(10.0) (Exynos990 chipsets) software. The S3K250AF Secure Element CC EAL 5+ chip allows attackers to execute arbitrary code and obtain sensitive information via a buffer overflow. The Samsung ID is SVE-2020-18632 (November 2020).

In multiple locations, there is a possible way that avdtp and avctp channels could be unencrypted due to a logic error in the code. This could lead to local escalation of privilege with User execution privileges needed. User interaction is not needed for exploitation.

In setAllowOnlyVpnForUids of NetworkManagementService.java, there is a possible security settings bypass due to a missing permission check. This could lead to local escalation of privilege allowing users to access non-VPN networks, when they are supposed to be restricted to the VPN networks, with no additional execution privileges needed. User interaction is not needed for exploitation.

In onStart of CompanionDeviceManagerService.java, there is a possible confused deputy 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 checkPermissions of RecognitionService.java, there is a possible permissions bypass 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 writeToParcel and createFromParcel of DcParamObject.java, there is a permission bypass due to a write size mismatch. This could lead to an elevation of privileges where the user can start an activity with system privileges, with no additional execution privileges needed. User interaction is not needed for exploitation.

In String16 of String16.cpp, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege in an unprivileged process with no additional execution privileges needed. User interaction is not needed for exploitation.

In createFromParcel of ViewPager.java, there is a possible read/write serialization issue leading to a permissions bypass. This could lead to local escalation of privilege where an app can start an activity with system privileges with no additional execution privileges needed. User interaction is not needed for exploitation.

In createFromParcel of MediaCas.java, there is a possible parcel read/write mismatch due to improper input validation. This could lead to local escalation of privilege where an app can start an activity with system privileges with no additional execution privileges needed. User interaction is not needed for exploitation.

In the read() function of ProcessStats.java, there is a possible read/write serialization issue leading to a permissions bypass. This could lead to local escalation of privilege where an app can start an activity with system privileges with no additional execution privileges needed. User interaction is not needed for exploitation.

In HidRawSensor::batch of HidRawSensor.cpp, there is a possible out of bounds write due to an unexpected switch fallthrough. 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-144040966

In create of FileMap.cpp, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10 Android-8.0 Android-8.1 Android-9Android ID: A-156997193

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 multiple locations, there is a possible way to overlay the installation confirmation dialog 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.

In srtd 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 trusty_ffa_mem_reclaim of shared-mem-smcall.c, there is a possible memory corruption due to uninitialized data. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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

In aoc_service_read_message of aoc_ipc_core.c, there is a possible out of bounds read due to improper input validation. This could lead to local escalation of privilege with System 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 firewall service, there is a missing permission check. This could lead to local escalation of privilege with system execution privileges needed.

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

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