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

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

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

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 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. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.SparseMatMul`. The division by 0 occurs deep in Eigen code because the `b` tensor is empty. 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.

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.QuantizeAndDequantizeV4Grad`. This is because the implementation does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to `QuantizeAndDequantizePerChannelGradientImpl`. However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK` failure otherwise. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version.

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

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division. Since `data` is given by the `values` argument, `num_batch_elements` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected.

An issue was discovered on Samsung mobile devices with software through 2016-05-27 (Exynos AP chipsets). A local graphics user can cause a Kernel Crash via the fb0(DECON) frame buffer interface. The Samsung ID is SVE-2016-7011 (October 2016).

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a division by zero to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L513-L522) computes a divisor based on user provided data (i.e., the shape of the tensors given as arguments). If all shapes are empty then `work_unit_size` is 0. Since there is no check for this case before division, this results in a runtime exception, with potential to be abused for a denial of service. 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 implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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 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.

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.

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 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.

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 null pointer dereference issue 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 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.

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 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 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 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 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 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.