TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L289-L295) computes a modulo operation without validating that the divisor is not zero. Since `vector_num_elements` is determined based on input shapes(https://github.com/tensorflow/tensorflow/blob/6f26b3f3418201479c264f2a02000880d8df151c/tensorflow/core/kernels/quantized_add_op.cc#L522-L544), a user can trigger scenarios where this quantity 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. An attacker can trigger a division by 0 in `tf.raw_ops.QuantizedMul`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55900e961ed4a23b438392024912154a2c2f5e85/tensorflow/core/kernels/quantized_mul_op.cc#L188-L198) 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. The API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements. Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. 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.

Calling of non-existent provider in Samsung Members prior to version 2.4.81.13 (in Android O(8.1) and below) and 3.8.00.13 (in Android P(9.0) and above) allows unauthorized actions including denial of service attack by hijacking the provider.

Assuming a shell privilege is gained, an improper exception handling for multi_sim_bar_show_on_qspanel value in SystemUI prior to SMR Oct-2021 Release 1 allows an attacker to cause a permanent denial of service in user device before factory reset.

In camera driver, there is a possible use after free due to a logic error. This could lead to local denial of service with System execution privileges needed

Nullptr dereference when a null char is present in a proto symbol. The symbol is parsed incorrectly, leading to an unchecked call into the proto file's name during generation of the resulting error message. Since the symbol is incorrectly parsed, the file is nullptr. We recommend upgrading to version 3.15.0 or greater.

In isp, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed. User interaction is needed for exploitation. Patch ID: ALPS09071481; Issue ID: MSV-1730.

In memory management driver, there is a possible system crash due to a missing bounds check. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05403499; Issue ID: ALPS05381071.

In addSubInfo of SubscriptionController.java, there is a possible way to force the user to make a factory reset due to a logic error in the code. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12Android ID: A-197327688

In memory management driver, there is a possible system crash due to a missing bounds check. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS05403499; Issue ID: ALPS05393787.

In gpu driver, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In gsp driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In gpu driver, there is a possible out of bounds write due to a incorrect bounds check. This could lead to local denial of service with System execution privileges needed

In gsp driver, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In gpu driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In gsp driver, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In gnss service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In TeleService, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In Gnss service, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In gpu driver, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In ril service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In gpu driver, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In gnss service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In jpg driver, there is a possible out of bounds write due to improper input validation. This could lead to local denial of service with System execution privileges needed

An issue was discovered on LG mobile devices with Android OS 7.0, 7.1, 7.2, 8.0, 8.1, and 9.0 software. A TrustZone trusted application can crash via crafted input. The LG ID is LVE-SMP-190003 (May 2019).

An issue was discovered on LG mobile devices with Android OS 7.0, 7.1, 7.2, 8.0, and 8.1 software. A TZ trusted application can crash via crafted input. The LG ID is LVE-SMP-190005 (July 2019).

In ril service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In ril service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In faceid service, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In ril service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

Improper input validationation for some Intel Unison software may allow a privileged user to potentially enable denial of service via local access.

In urild service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In urild service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In autotest driver, there is a possible out of bounds write due to improper input validation. This could lead to local denial of service with System execution privileges needed

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, there is a potential for segfault / denial of service in TensorFlow by calling `tf.compat.v1.*` ops which don't yet have support for quantized types, which was added after migration to TensorFlow 2.x. In these scenarios, since the kernel is missing, a `nullptr` value is passed to `ParseDimensionValue` for the `py_value` argument. Then, this is dereferenced, resulting in segfault. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

In media service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In jpg driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In drm driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In jpg driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

In vsp driver, there is a possible use after free due to a logic error. This could lead to local denial of service with System execution privileges needed

A denial of service vulnerability in Setup Wizard could allow a local attacker to require Google account sign-in after a factory reset. This issue is rated as Moderate because it may require a factory reset to repair the device. Product: Android. Versions: 5.1.1, 6.0, 6.0.1, 7.0, 7.1.1. Android ID: A-30352311.

In libimpl-ril, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.

In hci_server, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.

In libimpl-ril, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed.

fscrypt through v0.3.2 creates a world-writable directory by default when setting up a filesystem, allowing unprivileged users to exhaust filesystem space. We recommend upgrading to fscrypt 0.3.3 or above and adjusting the permissions on existing fscrypt metadata directories where applicable.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a denial of service via a segmentation fault in `tf.raw_ops.MaxPoolGrad` caused by missing validation. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/maxpooling_op.cc) misses some validation for the `orig_input` and `orig_output` tensors. The fixes for CVE-2021-29579 were incomplete. We have patched the issue in GitHub commit 136b51f10903e044308cf77117c0ed9871350475. 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 an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. This is caused by the MLIR optimization of `L2NormalizeReduceAxis` operator. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/compiler/mlir/lite/transforms/optimize.cc#L67-L70) unconditionally dereferences a pointer to an iterator to a vector without checking that the vector has elements. We have patched the issue in GitHub commit d6b57f461b39fd1aa8c1b870f1b974aac3554955. 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 an attacker can craft a TFLite model that would trigger a division by zero error in LSH [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/lsh_projection.cc#L118). We have patched the issue in GitHub commit 0575b640091680cfb70f4dd93e70658de43b94f9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick thiscommit 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 of `tf.raw_ops.StringNGrams` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls `reserve` on a `tstring` with a value that sometimes can be negative if user supplies negative `ngram_widths`. The `reserve` method calls `TF_TString_Reserve` which has an `unsigned long` argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer. We have patched the issue in GitHub commit c283e542a3f422420cfdb332414543b62fc4e4a5. 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.