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

An improper input validation vulnerability in loading graph file in DSP driver prior to SMR Sep-2021 Release 1 allows attackers to perform permanent denial of service on the device.

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 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.

An improper access control vulnerability in sspExit() in BlockchainTZService prior to SMR Sep-2021 Release 1 allows attackers to terminate BlockchainTZService.

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.

NULL pointer dereference vulnerability in NPU driver prior to SMR Sep-2021 Release 1 allows attackers to cause memory corruption.

Graphic format mismatch while converting video format in hwcomposer prior to SMR Mar-2021 Release 1 results in kernel panic due to unsupported format.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.CTCGreedyDecoder`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1615440b17b364b875eb06f43d087381f1460a65/tensorflow/core/kernels/ctc_decoder_ops.cc#L37-L50) has a `CHECK_LT` inserted to validate some invariants. When this condition is false, the program aborts, instead of returning a valid error to the user. This abnormal termination can be weaponized in denial of service attacks. 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 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 denial of service via a `CHECK` failure by passing an empty image to `tf.raw_ops.DrawBoundingBoxes`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/ea34a18dc3f5c8d80a40ccca1404f343b5d55f91/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L148-L165) uses `CHECK_*` assertions instead of `OP_REQUIRES` to validate user controlled inputs. Whereas `OP_REQUIRES` allows returning an error condition back to the user, the `CHECK_*` macros result in a crash if the condition is false, similar to `assert`. In this case, `height` is 0 from the `images` input. This results in `max_box_row_clamp` being negative and the assertion being falsified, followed by aborting program execution. 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.

NULL pointer dereference vulnerability in ION driver prior to SMR Sep-2021 Release 1 allows attackers to cause memory corruption.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a division by 0 in `tf.raw_ops.Conv2D`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/988087bd83f144af14087fe4fecee2d250d93737/tensorflow/core/kernels/conv_ops.cc#L261-L263) 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.

In camera driver, there is a possible memory corruption due to improper locking. 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.

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 end-to-end open source platform for machine learning. An attacker can cause a denial of service by exploiting a `CHECK`-failure coming from the implementation of `tf.raw_ops.RFFT`. Eigen code operating on an empty matrix can trigger on an assertion and will cause program termination. 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 isSlotMarkedSuccessful of BootControl.cpp, there is a possible out of bounds read due to a missing bounds check. This could lead to local  information disclosure with no additional execution privileges needed. User  interaction is not needed for exploitation.

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

In multiple locations, there is a possible way to create a large amount of app ops 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.

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 trigger a denial of service via a `CHECK`-fail in converting sparse tensors to CSR Sparse matrices. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/800346f2c03a27e182dd4fba48295f65e7790739/tensorflow/core/kernels/sparse/kernels.cc#L66) does a double redirection to access an element of an array allocated on the heap. If the value at `indices(i, 0)` is such that `indices(i, 0) + 1` is outside the bounds of `csr_row_ptr`, this results in writing outside of bounds of heap allocated data. 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.

There is a possible UAF due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In dhd_prot_flowrings_pool_release of dhd_msgbuf.c, there is a possible outcof bounds write due to a missing bounds check. This could lead to localcescalation of privilege with no additional execution privileges needed. Usercinteraction is not needed for exploitation.

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 imgsys, there is a possible memory corruption due to improper locking. This could lead to local denial of service if a malicious actor has already obtained the System privilege. User interaction is not needed for exploitation. Patch ID: ALPS10363254; Issue ID: MSV-5617.

In registerPhoneAccount of PhoneAccountRegistrar.java, there is a possible way to prevent the user from selecting a phone account due to improper input validation. 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-12LAndroid ID: A-217934478

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

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

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

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

In Music service, there is a missing permission check. This could lead to local denial of service in Music service with no additional 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 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 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.