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 scheme of Uri.java, there is a possible way to craft a malformed Uri object 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. 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.

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 wifi_item_edit_content of styles.xml , there is a possible FRP bypass due to Missing check for FRP state. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In clearApplicationUserData of ActivityManagerService.java, there is a possible way to remove system files due to a path traversal error. This could lead to local escalation of privilege with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10 Android-11 Android-12 Android-12L Android-13Android ID: A-240267890

In setTransactionState of SurfaceFlinger.cpp, there is a possible way to change protected display attributes 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 multiple functions of WifiNetworkFactory.java, there is a missing permission check. This could lead to local escalation of privilege from the guest user with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-266700762

In multiple functions of WifiCallingSettings.java, there is a possible way to change calling preferences for the admin user 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-13Android ID: A-262243015

Multiple integer overflows in the MDSS driver for the Linux kernel 3.x, as used in Qualcomm Innovation Center (QuIC) Android contributions for MSM devices and other products, allow attackers to cause a denial of service or possibly have unspecified other impact via a large size value, related to mdss_compat_utils.c, mdss_fb.c, and mdss_rotator.c.

Summary:Product: AndroidVersions: Android SoCAndroid ID: A-238257002

Insufficient policy enforcement in WebUI in Google Chrome prior to 124.0.6367.60 allowed a remote attacker to bypass content security policy via a crafted HTML page. (Chromium security severity: Low)

A buffer overflow vulnerability was discovered in Zoom Client for Meetings (for Android, iOS, Linux, macOS, and Windows) before version 5.8.4, Zoom Client for Meetings for Blackberry (for Android and iOS) before version 5.8.1, Zoom Client for Meetings for intune (for Android and iOS) before version 5.8.4, Zoom Client for Meetings for Chrome OS before version 5.0.1, Zoom Rooms for Conference Room (for Android, AndroidBali, macOS, and Windows) before version 5.8.3, Controllers for Zoom Rooms (for Android, iOS, and Windows) before version 5.8.3, Zoom VDI Windows Meeting Client before version 5.8.4, Zoom VDI Azure Virtual Desktop Plugins (for Windows x86 or x64, IGEL x64, Ubuntu x64, HP ThinPro OS x64) before version 5.8.4.21112, Zoom VDI Citrix Plugins (for Windows x86 or x64, Mac Universal Installer & Uninstaller, IGEL x64, eLux RP6 x64, HP ThinPro OS x64, Ubuntu x64, CentOS x 64, Dell ThinOS) before version 5.8.4.21112, Zoom VDI VMware Plugins (for Windows x86 or x64, Mac Universal Installer & Uninstaller, IGEL x64, eLux RP6 x64, HP ThinPro OS x64, Ubuntu x64, CentOS x 64, Dell ThinOS) before version 5.8.4.21112, Zoom Meeting SDK for Android before version 5.7.6.1922, Zoom Meeting SDK for iOS before version 5.7.6.1082, Zoom Meeting SDK for macOS before version 5.7.6.1340, Zoom Meeting SDK for Windows before version 5.7.6.1081, Zoom Video SDK (for Android, iOS, macOS, and Windows) before version 1.1.2, Zoom On-Premise Meeting Connector Controller before version 4.8.12.20211115, Zoom On-Premise Meeting Connector MMR before version 4.8.12.20211115, Zoom On-Premise Recording Connector before version 5.1.0.65.20211116, Zoom On-Premise Virtual Room Connector before version 4.4.7266.20211117, Zoom On-Premise Virtual Room Connector Load Balancer before version 2.5.5692.20211117, Zoom Hybrid Zproxy before version 1.0.1058.20211116, and Zoom Hybrid MMR before version 4.6.20211116.131_x86-64. This can potentially allow a malicious actor to crash the service or application, or leverage this vulnerability to execute arbitrary code.

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 the APEX module framework of AOSP, there is a possible malicious update to platform components due to improperly used crypto. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. More details on this can be found in the referenced links.

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.

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.

Inappropriate implementation in the ChromeOS Readiness Tool installer on Windows prior to 1.0.2.0 loosens DCOM access rights on two objects allowing an attacker to potentially bypass discretionary access controls.

In fixUpIncomingShortcutInfo of ShortcutService.java, there is a possible way to view another user's image 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 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 attributeBytesBase64 and attributeBytesHex of BinaryXmlSerializer.java, there is a possible arbitrary XML injection 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 com_android_internal_os_ZygoteCommandBuffer_nativeForkRepeatedly of com_android_internal_os_ZygoteCommandBuffer.cpp, there is a possible method to perform arbitrary code execution in any app zygote processes 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 setupVideoEncoder of StagefrightRecorder.cpp, there is a possible asynchronous playback when B-frame support is enabled. 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 permissions bypass due to a missing null check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In onForegroundServiceButtonClicked of FooterActionsViewModel.kt, there is a possible way to disable the active VPN app from the lockscreen due to an insecure default value. This could lead to local escalation of privilege with no additional 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 PVRSRV_MMap of pvr_bridge_k.c, there is a possible arbitrary code execution due to a logic error in the code. This could lead to local escalation of privilege in the kernel with no additional execution privileges needed. User interaction is not needed for exploitation.

In setTransactionState of SurfaceFlinger.cpp, there is a possible way to perform tapjacking 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 multiple locations, there is a possible bypass of user consent to enabling new Bluetooth HIDs 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 circ_read of link_device_memory_legacy.c, there is a possible out of bounds write due to an incorrect bounds check. This could lead to remote code execution with no additional execution privileges needed. User interaction is not needed for exploitation.

In sec_media_protect of media.c, there is a possible permission bypass due to a race condition. 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.

In AcvpOnMessage of avcp.cpp, there is a possible EOP 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.

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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of the `DepthToSpace` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/depth_to_space.cc#L63-L69). An attacker can craft a model such that `params->block_size` is 0. 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 the `OneHot` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/f61c57bd425878be108ec787f4d96390579fb83e/tensorflow/lite/kernels/one_hot.cc#L68-L72). An attacker can craft a model such that at least one of the dimensions of `indices` would be 0. In turn, the `prefix_dim_size` value would become 0. 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. 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.

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.

In lwis_add_completion_fence of lwis_fence.c, there is a possible escalation of privilege due to type confusion. 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.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of TrySimplify(https://github.com/tensorflow/tensorflow/blob/c22d88d6ff33031aa113e48aa3fc9aa74ed79595/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc#L390-L401) has undefined behavior due to dereferencing a null pointer in corner cases that result in optimizing a node with no inputs. 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 undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. 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 checkKeyIntentParceledCorrectly of AccountManagerService.java, there is a possible way to launch arbitrary activities using system privileges due to Parcel Mismatch. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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.

TensorFlow is an end-to-end open source platform for machine learning. The `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division(https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be zero. 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.

There is a possible escalation of privilege due to improperly used crypto. This could lead to remote escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.