In Bluetooth, there is a possible out of bounds read 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.Product: AndroidVersions: Android-12LAndroid ID: A-205989472

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.

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

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `MatrixTriangularSolve`(https://github.com/tensorflow/tensorflow/blob/8cae746d8449c7dda5298327353d68613f16e798/tensorflow/core/kernels/linalg/matrix_triangular_solve_op_impl.h#L160-L240) fails to terminate kernel execution if one validation condition fails. 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.

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 read 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 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat<T>()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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.

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 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 video decoder, there is a possible out of bounds read due to improper input validation. This could lead to local denial of service with no additional execution privileges needed

In video decoder, there is a possible out of bounds read due to improper input validation. This could lead to local denial of service with no additional execution privileges needed

In video service, there is a possible out of bounds read due to a incorrect bounds check. This could lead to local denial of service with no additional execution privileges needed

In video service, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with no additional execution privileges needed

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.

Improper buffer size check logic in aviextractor library prior to SMR May-2022 Release 1 allows out of bounds read leading to possible temporary denial of service. The patch adds buffer size check logic.

Improper buffer size check logic in wmfextractor library prior to SMR May-2022 Release 1 allows out of bounds read leading to possible temporary denial of service. The patch adds buffer size check logic.

Improper buffer size check logic in aviextractor library prior to SMR May-2022 Release 1 allows out of bounds read leading to possible temporary denial of service. The patch adds buffer size check logic.

Improper buffer size check logic in aviextractor library prior to SMR May-2022 Release 1 allows out of bounds read leading to possible temporary denial of service. The patch adds buffer size check logic.

In video decoder, there is a possible out of bounds read due to improper input validation. This could lead to local denial of service with no additional execution privileges needed

In video decoder, there is a possible out of bounds read due to improper input validation. This could lead to local denial of service with no additional execution privileges needed

In camera service, there is a possible out of bounds read due to a missing bounds check. This could lead to local denial of service with no additional execution privileges needed

Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming `GraphDef` before converting it to the MLIR-based dialect. If an attacker changes the `SavedModel` format on disk to invalidate these assumptions and the `GraphDef` is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.

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

In Messaging, There has unauthorized broadcast, this could cause Local Deny of Service.Product: AndroidVersions: Android SoCAndroid ID: A-242258929

In validateForCommonR1andR2 of PasspointConfiguration.java, uncaught errors in parsing stored configs could lead to local persistent denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12 Android-12L Android-13Android ID: A-246539931

In getAvailabilityStatus of PrivateDnsPreferenceController.java, there is a possible way for a guest user to change private DNS settings due to a permissions bypass. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10 Android-11 Android-12 Android-12LAndroid ID: A-206987762

In the Phone app, there is a possible crash loop due to resource exhaustion. This could lead to local persistent denial of service in the Phone app with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-220865698

In get of PacProxyService.java, there is a possible system service crash due to improper input validation. 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-219498290

In setImpl of AlarmManagerService.java, there is a possible way to put a device into a boot loop due to an uncaught exception. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10 Android-11 Android-12 Android-12L Android-13Android ID: A-234441463

TensorFlow is an open source platform for machine learning. In affected versions the implementation of `tf.math.segment_*` operations results in a `CHECK`-fail related abort (and denial of service) if a segment id in `segment_ids` is large. This is similar to CVE-2021-29584 (and similar other reported vulnerabilities in TensorFlow, localized to specific APIs): the implementation (both on CPU and GPU) computes the output shape using `AddDim`. However, if the number of elements in the tensor overflows an `int64_t` value, `AddDim` results in a `CHECK` failure which provokes a `std::abort`. Instead, code should use `AddDimWithStatus`. 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.

An issue was discovered in Finder on Samsung mobile devices with Q(10.0) software. A call to a non-existent provider allows attackers to cause a denial of service. The Samsung ID is SVE-2020-18629 (December 2020).

TensorFlow is an open source platform for machine learning. In affected versions if `tf.tile` 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 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.

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 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.

In onClick of MainClear.java, there is a possible way to trigger factory reset without explicit user consent due to a logic error in the code. 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 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.