In function msm_pcm_playback_close() in all Android releases from CAF using the Linux kernel, prtd is assigned substream->runtime->private_data. Later, prtd is freed. However, prtd is not sanitized and set to NULL, resulting in a dangling pointer. There are other functions that access the same memory (substream->runtime->private_data) with a NULL check, such as msm_pcm_volume_ctl_put(), which means this freed memory could be used.

In Android before the 2018-06-05 security patch level, NVIDIA TLZ TrustZone contains a possible out of bounds write due to integer overflow which could lead to local escalation of privilege in the TrustZone with no additional execution privileges needed. User interaction is not needed for exploitation. This issue is rated as high. Version: N/A. Android: A-69480285. Reference: N-CVE-2017-6292.

In Android before the 2018-06-05 security patch level, NVIDIA Tegra X1 TZ contains a possible out of bounds write due to missing bounds check which could lead to escalation of privilege from the kernel to the TZ. User interaction is not needed for exploitation. This issue is rated as high. Version: N/A. Android: A-69316825. Reference: N-CVE-2017-6294.

NVIDIA libnvmmlite_audio.so contains an elevation of privilege vulnerability when running in media server which may cause an out of bounds write and could lead to local code execution in a privileged process. This issue is rated as high. Product: Android. Version: N/A. Android: A-65023166. Reference: N-CVE-2017-6279.

NVIDIA libnvomx contains a possible out of bounds write due to a missing bounds check which could lead to local escalation of privilege. This issue is rated as high. Product: Android. Version: N/A. Android: A-64893247. Reference: N-CVE-2017-6286.

In Android before the 2018-05-05 security patch level, NVIDIA Tegra X1 TZ contains a vulnerability in Widevine TA where the software writes data past the end, or before the beginning, of the intended buffer, which may lead to escalation of Privileges. This issue is rated as high. Android: A-69377364. Reference: N-CVE-2017-6293.

NVIDIA driver contains a vulnerability where it is possible a use after free malfunction can occur due to improper usage of the list_for_each kernel macro which could enable unauthorized code execution and possibly lead to elevation of privileges. This issue is rated as high. Product: Android. Version: N/A. Android ID: A-38046353. References: N-CVE-2017-6263.

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 vring_init of external/headers/include/virtio/virtio_ring.h, there is a possible out of bounds write 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.

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 ppmp_unprotect_buf of drm/code/drm_fw.c, there is a possible memory corruption 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.

An issue was discovered on Samsung mobile devices with P(9.0) software. There is a heap overflow in the knox_kap driver. The Samsung ID is SVE-2019-14857 (November 2019).

TensorFlow is an open source platform for machine learning. In affected versions the async implementation of `CollectiveReduceV2` suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.

In SendMediaUpdate and SendFolderUpdate of avrcp_service.cc, there is a possible memory corruption due to a use after free. This could lead to local escalation of privilege in the Bluetooth service with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android Versions: Android-9 Android ID: A-120445479

In fs/eventpoll.c, there is a possible use after free. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-204450605References: Upstream kernel

In Keymaster, 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-173567719

In inotify_cb of events.cpp, 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.Product: AndroidVersions: Android-11 Android-12Android ID: A-202159709

In aio_poll_complete_work of aio.c, there is a possible memory corruption due to a use after free. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-185125206References: Upstream kernel

In DevmemValidateFlags of devicemem_server.c , 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 ipcSetDataReference of Parcel.cpp, there is a possible way to corrupt memory due to a use after free. 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-12Android ID: A-203847542

In kbase_jd_user_buf_pin_pages of mali_kbase_mem.c, there is a possible out of bounds write 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 kernelAndroid ID: A-210470189References: N/A

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 resizeToAtLeast of SkRegion.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.

In skia_alloc_func of SkDeflate.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.

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.

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 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.

In getConfig of SoftVideoDecoderOMXComponent.cpp, 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.

In ufshc_scsi_cmd of ufs.c, there is a possible stack variable use after free due to a use after free. 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 btm_ble_gap.cc, there is a possible out of bounds write 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.

In vring_size of external/headers/include/virtio/virtio_ring.h, 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.

In prepare_response_locked of lwis_transaction.c, there is a possible out of bounds write 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.

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. 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.

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.

In sendDeviceState_1_6 of RadioExt.cpp, there is a possible use after free due to improper locking. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In BCMFASTPATH of dhd_msgbuf.c, 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.

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.

In v4l2_smfc_qbuf of smfc-v4l2-ioctls.c, 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can write outside the bounds of heap allocated arrays by passing invalid arguments to `tf.raw_ops.Dilation2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/afd954e65f15aea4d438d0a219136fc4a63a573d/tensorflow/core/kernels/dilation_ops.cc#L321-L322) does not validate before writing to the output array. The values for `h_out` and `w_out` are guaranteed to be in range for `out_backprop` (as they are loop indices bounded by the size of the array). However, there are no similar guarantees relating `h_in_max`/`w_in_max` and `in_backprop`. 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 lwis_fence_signal of lwis_debug.c, there is a possible Use after Free due to improper locking. This could lead to local escalation of privilege from hal_camera_default SELinux label with no additional execution privileges needed. User interaction is not needed for exploitation.

In createWithSurfaceParent of Client.cpp, there is a possible out of bounds write due to type confusion. This could lead to local escalation of privilege in the graphics server with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10Android ID: A-150226994

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 cause a heap buffer overflow in `QuantizedResizeBilinear` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/50711818d2e61ccce012591eeb4fdf93a8496726/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L705-L706) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. 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 increment_annotation_count of stats_event.c, 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.

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.

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.

In Android for MSM, Firefox OS for MSM, QRD Android, with all Android releases from CAF using the Linux kernel, a crafted binder request can cause an arbitrary unmap in MediaServer.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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 multiple functions of StatsService.cpp, there is a possible memory corruption due to a use after free. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.