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.

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.

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).

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 prepare_io_entry and prepare_response of lwis_ioctl.c and lwis_periodic_io.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-205995773References: N/A

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 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 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 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

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

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.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.

NVIDIA Tegra kernel driver contains a vulnerability in NVIDIA NVDEC, where a user with high privileges might be able to read from or write to a memory location that is outside the intended boundary of the buffer, which may lead to denial of service, Information disclosure, loss of Integrity, or possible escalation of privileges.

Possible memory corruption due to improper validation of memory address while processing user-space IOCTL for clearing Filter and Route statistics in Snapdragon Auto, Snapdragon Compute, Snapdragon Connectivity, Snapdragon Consumer IOT, Snapdragon Industrial IOT, Snapdragon Mobile, Snapdragon Voice & Music, Snapdragon Wearables

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.

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. 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.

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.

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.

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 libmediadrm, there is an out-of-bounds write due to improper input validation. This could lead to local elevation of privileges with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: 8.0, 8.1. Android ID: A-67962232.

An improper input validation vulnerability in NPU firmware prior to SMR MAY-2021 Release 1 allows arbitrary memory write and code execution.

In ashmem_ioctl of ashmem.c, there is an out-of-bounds write due to insufficient locking when accessing asma. This could lead to a local elevation of privilege enabling code execution as a privileged process with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: Android kernel. Android ID: A-66954097.

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

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

In the nfc_hci_cmd_received() function of core.c, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege in the kernel with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: Android kernel. Android ID: A-62679701.

In CameraDeviceClient::submitRequestList of CameraDeviceClient.cpp, there is an out-of-bounds write if metadataSize is too small. This could lead to a local elevation of privilege enabling code execution as a privileged process with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: 5.1.1, 6.0, 6.0.1, 7.0, 7.1.1, 7.1.2, 8.0, 8.1. Android ID: A-67782345.

In the onQueueFilled function of SoftAVCDec, there is a possible out-of-bounds write due to a use after free if a bad header causes the decoder to get caught in a loop while another thread frees the memory it's accessing. This could lead to a local elevation of privilege enabling code execution as a privileged process with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: 6.0, 6.0.1, 7.0, 7.1.1, 7.1.2, 8.0, 8.1. Android ID: A-66969349.

In DisplayFtmItem in the bootloader, there is an out-of-bounds write due to reading a string without verifying that it's null-terminated. This could lead to a secure boot bypass and a local elevation of privilege enabling code execution as a privileged process with no additional execution privileges needed. User interaction is not needed for exploitation. Product: Android. Versions: Android kernel. Android ID: A-68269077.

An elevation of privilege vulnerability in the kernel v4l2 video driver. Product: Android. Versions: Android kernel. Android ID A-34624167.

In setClientStateLocked of SurfaceFlinger.cpp, there is a possible out of bounds write 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-12Android ID: A-193034683

In sound_trigger_event_alloc of platform.h, 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.Product: AndroidVersions: Android kernelAndroid ID: A-167663878

In halWrapperDataCallback of hal_wrapper.cc, 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-11Android ID: A-169328517

In apusys, 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. Patch ID: ALPS05672107; Issue ID: ALPS05672107.

In apusys, 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. Patch ID: ALPS05672107; Issue ID: ALPS05671206.

In parseExclusiveStateAnnotation of LogEvent.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.Product: AndroidVersions: Android-11Android ID: A-174488848

In netdiag, 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: Android; Versions: Android-10, Android-11; Patch ID: ALPS05442011.

In asf extractor, 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.Product: AndroidVersions: Android SoCAndroid ID: A-187231635

In the FingerTipS touch screen driver, there is a possible out of bounds write due to a heap buffer 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-157155375

In flv extractor, 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.Product: AndroidVersions: Android SoCAndroid ID: A-187236084

In kisd, there is a possible memory corruption due to a heap buffer overflow. This could lead to local escalation of privilege with System execution privileges needed. User interaction is not needed for exploitation. Product: Android; Versions: Android-11; Patch ID: ALPS05425247.

In setPowerModeWithHandle of com_android_server_power_PowerManagerService.cpp, 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.Product: AndroidVersions: Android-11Android ID: A-174243830