In log service, there is a missing permission check. This could lead to local denial of service in log service.

In telecom service, there is a missing permission check. This could lead to local denial of service in telecom service.

In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

In media service, there is a missing permission check. This could lead to local denial of service in media service.

In vowifiservice, there is a possible missing permission check.This could lead to local denial of service with no additional execution privileges

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementations of pooling in TFLite are vulnerable to division by 0 errors as there are no checks for divisors not being 0. We have patched the issue in GitHub commit [dfa22b348b70bb89d6d6ec0ff53973bacb4f4695](https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695). 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 cause a denial of service in `boosted_trees_create_quantile_stream_resource` by using negative arguments. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantile_ops.cc#L96) does not validate that `num_streams` only contains non-negative numbers. In turn, [this results in using this value to allocate memory](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/boosted_trees/quantiles/quantile_stream_resource.h#L31-L40). However, `reserve` receives an unsigned integer so there is an implicit conversion from a negative value to a large positive unsigned. This results in a crash from the standard library. We have patched the issue in GitHub commit 8a84f7a2b5a2b27ecf88d25bad9ac777cd2f7992. 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 log service, there is a missing permission check. This could lead to local denial of service in log service.

In telecom service, there is a missing permission check. This could lead to local denial of service in telecom service.

In engineermode services, there is a missing permission check. This could lead to local denial of service in engineermode services.

In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

In wlan driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in wlan services.

In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

In telecom service, there is a missing permission check. This could lead to local denial of service in telecom service.

In log service, there is a missing permission check. This could lead to local denial of service in log service.

In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

In gpu device, there is a memory corruption due to a use after free. This could lead to local denial of service in kernel.

In wcn service, there is a possible missing params check. This could lead to local denial of service in wcn service.

In h265 codec firmware, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with no additional execution privileges.

In log service, there is a missing permission check. This could lead to local denial of service in log service.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of division in TFLite is [vulnerable to a division by 0 error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/div.cc). There is no check that the divisor tensor does not contain zero elements. We have patched the issue in GitHub commit 1e206baedf8bef0334cca3eb92bab134ef525a28. 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 engineermode services, there is a missing permission check. This could lead to local denial of service in engineermode services.

In wlan driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in wlan services.

In engineermode services, there is a missing permission check. This could lead to local denial of service in engineermode services.

In engineermode services, there is a missing permission check. This could lead to local denial of service in engineermode services.

In telecom service, there is a missing permission check. This could lead to local denial of service in telecom service.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can trigger a crash via a floating point exception in `tf.raw_ops.ResourceGather`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L725-L731) computes the value of a value, `batch_size`, and then divides by it without checking that this value is not 0. We have patched the issue in GitHub commit ac117ee8a8ea57b73d34665cdf00ef3303bc0b11. 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 wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.

In messaging service, there is a missing permission check. This could lead to local denial of service in contacts service with no additional execution privileges needed.

In messaging service, there is a missing permission check. This could lead to local denial of service in contacts service with no additional execution privileges needed.

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.

In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.

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.

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. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. We have patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c. 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 messaging service, there is a missing permission check. This could lead to local denial of service in contacts service with no additional execution privileges needed.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions most implementations of convolution operators in TensorFlow are affected by a division by 0 vulnerability where an attacker can trigger a denial of service via a crash. The shape inference [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/framework/common_shape_fns.cc#L577) is missing several validations before doing divisions and modulo operations. We have patched the issue in GitHub commit 8a793b5d7f59e37ac7f3cd0954a750a2fe76bad4. 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 messaging service, there is a missing permission check. This could lead to local denial of service in contacts service with no additional execution privileges needed.

TensorFlow is an end-to-end open source platform for machine learning. The code for `tf.raw_ops.UncompressElement` can be made to trigger a null pointer dereference. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/compression_ops.cc#L50-L53) obtains a pointer to a `CompressedElement` from a `Variant` tensor and then proceeds to dereference it for decompressing. There is no check that the `Variant` tensor contained a `CompressedElement`, so the pointer is actually `nullptr`. We have patched the issue in GitHub commit 7bdf50bb4f5c54a4997c379092888546c97c3ebd. 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 wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.

In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.

In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.

In music service, there is a missing permission check. This could lead to local denial of service in contacts service with no additional execution privileges needed.

In vowifiservice, there is a possible missing permission check.This could lead to local denial of service with no additional execution privileges

In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.

In modem, there is a possible missing verification of HashMME value in Security Mode Command. This could local denial of service with no additional execution privileges.

In modem, there is a possible missing verification of NAS Security Mode Command Replay Attacks in LTE. This could local denial of service with no additional execution privileges.

In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.

In music service, there is a missing permission check. This could lead to local denial of service in contacts service with no additional execution privileges needed.