In wlan driver, there is a possible missing params 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 several methods of JobStore.java, uncaught exceptions in job map parsing could lead to local persistent denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-12 Android-12L Android-13Android ID: A-246541702
In addNetwork of WifiManager.java, there is a possible way to trigger a persistent DoS 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.Product: AndroidVersions: Android-13Android ID: A-244713323
In Policy of Policy.java, there is a possible boot loop 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.
An issue was discovered on LG mobile devices with Android OS 7.0, 7.1, 7.2, 8.0, 8.1, and 9.0 software. A TrustZone trusted application can crash via crafted input. The LG ID is LVE-SMP-190003 (May 2019).
In PreferencesHelper.java, an uncaught exception may cause the device to get stuck in a boot loop. This could lead to local persistent denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-12 Android-12L Android-13Android ID: A-261723753
In multiple locations, there is a possible way to trigger a persistent reboot loop 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-13Android ID: A-246749936
In setMediaButtonBroadcastReceiver of MediaSessionRecord.java, there is a possible permanent DoS 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.
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 multiple functions of JobStore.java, there is a possible way to cause a crash on startup 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-11 Android-12 Android-12L Android-13Android ID: A-246542285
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 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 setMimeGroup of PackageManagerService.java, there is a possible crash loop 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.Product: AndroidVersions: Android-11 Android-12 Android-12L Android-13Android ID: A-237291548
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 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 validatePassword of WifiConfigurationUtil.java, there is a possible way to get the device into a boot loop 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.
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 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. 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 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 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.