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 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 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 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 params 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 wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In npu driver, there is a memory corruption due to a use after free. This could lead to local denial of service in kernel.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
TensorFlow is an open source platform for machine learning. If a list of quantized tensors is assigned to an attribute, the pywrap code fails to parse the tensor and returns a `nullptr`, which is not caught. An example can be seen in `tf.compat.v1.extract_volume_patches` by passing in quantized tensors as input `ksizes`. We have patched the issue in GitHub commit e9e95553e5411834d215e6770c81a83a3d0866ce. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.
In addAutomaticZenRule of ZenModeHelper.java, there is a possible permanent denial of service due to resource exhaustion. This could lead to local denial of service with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10 Android-11 Android-12 Android-12LAndroid ID: A-220735360
In Messaging, There has unauthorized provider, this could cause Local Deny of Service.Product: AndroidVersions: Android SoCAndroid ID: A-242266172
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.
In wlan driver, there is a possible missing bounds check, This could lead to local denial of service in wlan services.
In face detect driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In camera driver, there is a possible memory corruption due to improper locking. This could lead to local denial of service in kernel.
In camera driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In face detect driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In wlan driver, there is a possible missing params check. This could lead to local denial of service in wlan services.
In face detect driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
In sensor driver, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service in kernel.
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 missing params check. This could lead to local denial of service in wlan services.
Improper Authorization vulnerability in setDualDARPolicyCmd prior to SMR Sep-2022 Release 1 allows local attackers to cause local permanent denial of service.
In multiple functions of AppOpsService.java, there is a possible way to saturate the content of /data/system/appops_accesses.xml due to resource exhaustion. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.
TensorFlow is an open source platform for machine learning. In affected versions the implementation of `ParallelConcat` misses some input validation and can produce a division by 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
TensorFlow is an open source platform for machine learning. In affected versions the implementations for convolution operators trigger a division by 0 if passed empty filter tensor arguments. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's Grappler optimizer has a use of unitialized variable. If the `train_nodes` vector (obtained from the saved model that gets optimized) does not contain a `Dequeue` node, then `dequeue_node` is left unitialized. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
TensorFlow is an open source platform for machine learning. In affected versions the implementation of `SplitV` can trigger a segfault is an attacker supplies negative arguments. This occurs whenever `size_splits` contains more than one value and at least one value is negative. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `AllToAll` can be made to execute a division by 0. This occurs whenever the `split_count` argument is 0. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
TensorFlow is an open source platform for machine learning. In affected versions while calculating the size of the output within the `tf.range` kernel, there is a conditional statement of type `int64 = condition ? int64 : double`. Due to C++ implicit conversion rules, both branches of the condition will be cast to `double` and the result would be truncated before the assignment. This result in overflows. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
TensorFlow is an open source platform for machine learning. In affected versions if `tf.image.resize` is called with a large input argument then the TensorFlow process will crash due to a `CHECK`-failure caused by an overflow. The number of elements in the output tensor is too much for the `int64_t` type and the overflow is detected via a `CHECK` statement. This aborts the process. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
TensorFlow is an open source platform for machine learning. In affected versions TensorFlow allows tensor to have a large number of dimensions and each dimension can be as large as desired. However, the total number of elements in a tensor must fit within an `int64_t`. If an overflow occurs, `MultiplyWithoutOverflow` would return a negative result. In the majority of TensorFlow codebase this then results in a `CHECK`-failure. Newer constructs exist which return a `Status` instead of crashing the binary. This is similar to CVE-2021-29584. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
TensorFlow is an open source platform for machine learning. In affected versions the shape inference function for `Transpose` is vulnerable to a heap buffer overflow. This occurs whenever `perm` contains negative elements. The shape inference function does not validate that the indices in `perm` are all valid. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.