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. 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 TFLite computation for size of output after padding, `ComputeOutSize`(https://github.com/tensorflow/tensorflow/blob/0c9692ae7b1671c983569e5d3de5565843d500cf/tensorflow/lite/kernels/padding.h#L43-L55), does not check that the `stride` argument is not 0 before doing the division. Users can craft special models such that `ComputeOutSize` is called with `stride` set to 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. In eager mode (default in TF 2.0 and later), session operations are invalid. However, users could still call the raw ops associated with them and trigger a null pointer dereference. The implementation(https://github.com/tensorflow/tensorflow/blob/eebb96c2830d48597d055d247c0e9aebaea94cd5/tensorflow/core/kernels/session_ops.cc#L104) dereferences the session state pointer without checking if it is valid. Thus, in eager mode, `ctx->session_state()` is nullptr and the call of the member function is undefined behavior. 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 `BatchToSpaceNd` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/b5ed552fe55895aee8bd8b191f744a069957d18d/tensorflow/lite/kernels/batch_to_space_nd.cc#L81-L82). An attacker can craft a model such that one dimension of the `block` input is 0. Hence, the corresponding value in `block_shape` 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 `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.

TensorFlow is an end-to-end open source platform for machine learning. TFLite's convolution code(https://github.com/tensorflow/tensorflow/blob/09c73bca7d648e961dd05898292d91a8322a9d45/tensorflow/lite/kernels/conv.cc) has multiple division where the divisor is controlled by the user and not checked to be non-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.

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.

Use after free in Browser UI in Google Chrome on Chrome prior to 92.0.4515.131 allowed a remote attacker to potentially exploit heap corruption via physical access to the device.

TensorFlow is an end-to-end open source platform for machine learning. Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences. The conversion from Python array to C++ array(https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion. 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 division by 0 in `tf.raw_ops.Conv2DBackpropInput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/b40060c9f697b044e3107917c797ba052f4506ab/tensorflow/core/kernels/conv_grad_input_ops.h#L625-L655) does a division by a quantity that is controlled by the caller. 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.

An issue was discovered on LG mobile devices with Android OS 11 software. They mishandle fingerprint recognition because local high beam mode (LHBM) does not function properly during bright illumination. The LG ID is LVE-SMP-210001 (March 2021).

An elevation of privilege vulnerability in the NVIDIA GPU driver. Product: Android. Versions: Android kernel. Android ID: A-31799863. References: N-CVE-2016-8482.

Possible buffer overflow in the hypervisor. Inappropriate usage of a static array could lead to a buffer overrun. Product: Android. Versions: Kernel 3.18. Android ID: A-31625904. References: QC-CR#1027769.

Possible unauthorized memory access in the hypervisor. Incorrect configuration provides access to subsystem page tables. Product: Android. Versions: Kernel 3.18. Android ID: A-32576499. References: QC-CR#964185.

NVIDIA Shield TV Experience prior to v8.0, contains a vulnerability in the custom NVIDIA API used in the mount system service where user data could be overridden, which may lead to code execution, denial of service, or information disclosure.

Possible unauthorized memory access in the hypervisor. Lack of input validation could allow hypervisor memory to be accessed by the HLOS. Product: Android. Versions: Kernel 3.18. Android ID: A-31625910. QC-CR#1038173.

Integer overflow in nvhost_job.c in the NVIDIA video driver for Android, Shield TV before OTA 3.3, Shield Table before OTA 4.4, and Shield Table TK1 before OTA 1.5 allows local users to cause a denial of service (system crash) via unspecified vectors, which triggers a buffer overflow.

Buffer overflow in nvhost_job.c in the NVIDIA video driver for Android, Shield TV before OTA 3.3, Shield Table before OTA 4.4, and Shield Table TK1 before OTA 1.5.

Stack-based buffer overflow in nvhost_job.c in the NVIDIA video driver for Android, Shield TV before OTA 3.3, Shield Table before OTA 4.4, and Shield Table TK1 before OTA 1.5.

In NetworkPolicyManagerService, there is a possible permissions bypass due to a missing permission check. This could lead to local escalation of privilege allowing a malicious app to modify the device's data plan with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11Android ID: A-148627993

In ic_startRetrieveEntryValue of acropora/app/identity/ic.c, there is a possible bypass of defense-in-depth due to missing validation of the return value. 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-195573629References: N/A

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.

NVIDIA Shield TV Experience prior to v8.0, NVIDIA Tegra bootloader contains a vulnerability in nvtboot where the Trusted OS image is improperly authenticated, which may lead to code execution, denial of service, escalation of privileges, and information disclosure, code execution, denial of service, or escalation of privileges

Using unsafe PendingIntent in Samsung Account in versions 10.8.0.4 in Android P(9.0) and below, and 12.1.1.3 in Android Q(10.0) and above allows local attackers to perform unauthorized action without permission via hijacking the PendingIntent.

Hijacking vulnerability in Samsung Email application version prior to SMR Feb-2021 Release 1 allows attackers to intercept when the provider is executed.

An improper validation vulnerability in telephony prior to SMR Dec-2021 Release 1 allows attackers to launch certain activities.

Improper input validation vulnerability in AR Emoji Editor prior to version 4.4.03.5 in Android Q(10.0) and above allows untrusted applications to access arbitrary files with an escalated privilege.

Improper access control vulnerability in Samsung Members prior to versions 2.4.85.11 in Android O(8.1) and below, and 3.9.10.11 in Android P(9.0) and above allows untrusted applications to cause local file inclusion in webview.

Improper sanitization of incoming intent in Samsung Contacts prior to SMR JUN-2021 Release 1 allows local attackers to copy or overwrite arbitrary files with Samsung Contacts privilege.

Improper validation check vulnerability in PackageManager prior to SMR July-2021 Release 1 allows untrusted applications to get dangerous level permission without user confirmation in limited circumstances.

Improper input validation vulnerability in HDCP prior to SMR Nov-2021 Release 1 allows attackers to arbitrary code execution.

An improper validation vulnerability in FilterProvider prior to SMR Dec-2021 Release 1 allows attackers to write arbitrary files via a path traversal vulnerability.

An improper validation vulnerability in FilterProvider prior to SMR Dec-2021 Release 1 allows local arbitrary code execution.

A use after free vulnerability in conn_gadget driver prior to SMR AUG-2021 Release 1 allows malicious action by an attacker.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions it is possible to nest a `tf.map_fn` within another `tf.map_fn` call. However, if the input tensor is a `RaggedTensor` and there is no function signature provided, code assumes the output is a fully specified tensor and fills output buffer with uninitialized contents from the heap. The `t` and `z` outputs should be identical, however this is not the case. The last row of `t` contains data from the heap which can be used to leak other memory information. The bug lies in the conversion from a `Variant` tensor to a `RaggedTensor`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/ragged_tensor_from_variant_op.cc#L177-L190) does not check that all inner shapes match and this results in the additional dimensions. The same implementation can result in data loss, if input tensor is tweaked. We have patched the issue in GitHub commit 4e2565483d0ffcadc719bd44893fb7f609bb5f12. 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 improper error handling in Exynos CP booting driver prior to SMR Oct-2021 Release 1 allows local attackers to bypass a Secure Memory Protector of Exynos CP Memory.

An improper input validation vulnerability in LDFW prior to SMR Dec-2021 Release 1 allows attackers to perform arbitrary code execution.

Insufficient restrictions on what can be done with Apple Events in Google Chrome on macOS prior to 72.0.3626.81 allowed a local attacker to execute JavaScript via Apple Events.

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

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

A possible stack-based buffer overflow vulnerability in Widevine trustlet prior to SMR Oct-2021 Release 1 allows arbitrary code execution.

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

An improper boundary check in secure_log of LDFW and BL31 prior to SMR Dec-2021 Release 1 allows arbitrary memory write and code execution.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause undefined behavior via binding a reference to null pointer in all operations of type `tf.raw_ops.MatrixSetDiagV*`. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/linalg/matrix_diag_op.cc) has incomplete validation that the value of `k` is a valid tensor. We have check that this value is either a scalar or a vector, but there is no check for the number of elements. If this is an empty tensor, then code that accesses the first element of the tensor is wrong. We have patched the issue in GitHub commit ff8894044dfae5568ecbf2ed514c1a37dc394f1b. 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. 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.

CF CLI version prior to v6.45.0 (bosh release version 1.16.0) writes the client id and secret to its config file when the user authenticates with --client-credentials flag. A local authenticated malicious user with access to the CF CLI config file can act as that client, who is the owner of the leaked credentials.

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.