Null pointer dereference in `MatrixDiagPartOp` in TensorFlow

TensorFlow is an end-to-end open source platform for machine learning. If a user does not provide a valid padding value to `tf.raw_ops.MatrixDiagPartOp`, then the code triggers a null pointer dereference (if input is empty) or produces invalid behavior, ignoring all values after the first. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/linalg/matrix_diag_op.cc#L89) reads the first value from a tensor buffer without first checking that the tensor has values to read from. We have patched the issue in GitHub commit 482da92095c4d48f8784b1f00dda4f81c28d2988. 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.

Division by 0 in `ResourceScatterDiv` in TensorFlow

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.ResourceScatterDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/resource_variable_ops.cc#L865) uses a common class for all binary operations but fails to treat the division by 0 case separately. We have patched the issue in GitHub commit 4aacb30888638da75023e6601149415b39763d76. 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.

Heap OOB in `RaggedGather` in TensorFlow

TensorFlow is an end-to-end open source platform for machine learning. In affected versions if the arguments to `tf.raw_ops.RaggedGather` don't determine a valid ragged tensor code can trigger a read from outside of bounds of heap allocated buffers. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/ragged_gather_op.cc#L70) directly reads the first dimension of a tensor shape before checking that said tensor has rank of at least 1 (i.e., it is not a scalar). Furthermore, the implementation does not check that the list given by `params_nested_splits` is not an empty list of tensors. We have patched the issue in GitHub commit a2b743f6017d7b97af1fe49087ae15f0ac634373. 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.

Integer division by 0 in sparse reshaping in TensorFlow

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.SparseReshape` can be made to trigger an integral division by 0 exception. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L176-L181) calls the reshaping functor whenever there is at least an index in the input but does not check that shape of the input or the target shape have both a non-zero number of elements. The [reshape functor](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/reshape_util.cc#L40-L78) blindly divides by the dimensions of the target shape. Hence, if this is not checked, code will result in a division by 0. We have patched the issue in GitHub commit 4923de56ec94fff7770df259ab7f2288a74feb41. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1 as this is the other affected version.

Null pointer dereference and heap OOB read in TensorFlow

TensorFlow is an end-to-end open source platform for machine learning. When restoring tensors via raw APIs, if the tensor name is not provided, TensorFlow can be tricked into dereferencing a null pointer. Alternatively, attackers can read memory outside the bounds of heap allocated data by providing some tensor names but not enough for a successful restoration. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/kernels/save_restore_tensor.cc#L158-L159) retrieves the tensor list corresponding to the `tensor_name` user controlled input and immediately retrieves the tensor at the restoration index (controlled via `preferred_shard` argument). This occurs without validating that the provided list has enough values. If the list is empty this results in dereferencing a null pointer (undefined behavior). If, however, the list has some elements, if the restoration index is outside the bounds this results in heap OOB read. We have patched the issue in GitHub commit 9e82dce6e6bd1f36a57e08fa85af213e2b2f2622. 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.

Null pointer dereference in `RaggedTensorToTensor` in TensorFlow

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.

Null pointer dereference in `CompressElement` in TensorFlow

TensorFlow is an end-to-end open source platform for machine learning. It is possible to trigger a null pointer dereference in TensorFlow by passing an invalid input to `tf.raw_ops.CompressElement`. The [implementation](https://github.com/tensorflow/tensorflow/blob/47a06f40411a69c99f381495f490536972152ac0/tensorflow/core/data/compression_utils.cc#L34) was accessing the size of a buffer obtained from the return of a separate function call before validating that said buffer is valid. We have patched the issue in GitHub commit 5dc7f6981fdaf74c8c5be41f393df705841fb7c5. 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.

Floating point exception in `SparseDenseCwiseDiv` in TensorFlow

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of `tf.raw_ops.SparseDenseCwiseDiv` is vulnerable to a division by 0 error. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_dense_binary_op_shared.cc#L56) uses a common class for all binary operations but fails to treat the division by 0 case separately. We have patched the issue in GitHub commit d9204be9f49520cdaaeb2541d1dc5187b23f31d9. 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.

Heap out of bounds access in sparse reduction operations in TensorFlow

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of sparse reduction operations in TensorFlow can trigger accesses outside of bounds of heap allocated data. The [implementation](https://github.com/tensorflow/tensorflow/blob/a1bc56203f21a5a4995311825ffaba7a670d7747/tensorflow/core/kernels/sparse_reduce_op.cc#L217-L228) fails to validate that each reduction group does not overflow and that each corresponding index does not point to outside the bounds of the input tensor. We have patched the issue in GitHub commit 87158f43f05f2720a374f3e6d22a7aaa3a33f750. 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 through 2.5.0 allows attackers to overwrite arbitrary files via a crafted archive when tf.keras.utils.get_file is used with extract=True. NOTE: the vendor's position is that tf.keras.utils.get_file is not intended for untrusted archives

Server Side Request Forgery in Zoom Client for Meetings chat

The Zoom Client for Meetings before version 5.7.3 (for Android, iOS, Linux, macOS, and Windows) contain a server side request forgery vulnerability in the chat\'s "link preview" functionality. In versions prior to 5.7.3, if a user were to enable the chat\'s "link preview" feature, a malicious actor could trick the user into potentially sending arbitrary HTTP GET requests to URLs that the actor cannot reach directly.

Process memory exposure in Zoom Client and other products

A vulnerability was discovered in the 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 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 which potentially allowed for the exposure of the state of process memory. This issue could be used to potentially gain insight into arbitrary areas of the product's memory.

Buffer overflow in Zoom client and other products

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.

kernel: Race condition in NVIDIA driver can lead to a null pointer dereference

NVIDIA Tegra kernel driver contains a vulnerability in NVHost, where a specific race condition can lead to a null pointer dereference, which may lead to a system reboot.

NVIDIA Linux distributions contain a vulnerability in TrustZone’s TEE_Malloc function, where an unchecked return value causing a null pointer dereference may lead to denial of service.

Android images for T210 provided by NVIDIA contain a vulnerability in BROM, where failure to limit access to AHB-DMA when BROM fails may allow an unprivileged attacker with physical access to cause denial of service or impact integrity and confidentiality beyond the security scope of BROM.

kernel: NVIDIA Linux use after free in nvmap ioctl

NVIDIA Linux distributions contain a vulnerability in nvmap ioctl, which allows any user with a local account to exploit a use-after-free condition, leading to code privilege escalation, loss of confidentiality and integrity, or denial of service.

kernel: NVIDIA Tegra kernel driver contains a vulnerability in NVIDIA NVDEC

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.

kernel: NVIDIA driver improper access control may lead to code execution

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.

GAEN (aka Google/Apple Exposure Notifications) through 2021-04-27 on Android allows attackers to obtain sensitive information, such as a user's location history, in-person social graph, and (sometimes) COVID-19 infection status, because Rolling Proximity Identifiers and MAC addresses are written to the Android system log, and many Android devices have applications (preinstalled by the hardware manufacturer or network operator) that read system log data and send it to third parties. NOTE: a news outlet (The Markup) states that they received a vendor response indicating that fix deployment "began several weeks ago and will be complete in the coming days."