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 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.

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 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.

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.AddManySparseToTensorsMap`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/kernels/sparse_tensors_map_ops.cc#L257) takes the values specified in `sparse_shape` as dimensions for the output shape. The `TensorShape` constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when `InitDims`(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. 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 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 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.

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

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.

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 open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.raw_ops.SpaceToBatchND` (in all backends such as XLA and handwritten kernels) is vulnerable to an integer overflow: The result of this integer overflow is used to allocate the output tensor, hence we get a denial of service via a `CHECK`-failure (assertion failure), as in TFSA-2021-198. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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 caused by an integer overflow in constructing a new tensor shape. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/0908c2f2397c099338b901b067f6495a5b96760b/tensorflow/core/kernels/sparse_split_op.cc#L66-L70) builds a dense shape without checking that the dimensions would not result in overflow. The `TensorShape` constructor(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when `InitDims`(https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. 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 TFLite code for allocating `TFLiteIntArray`s is vulnerable to an integer overflow issue(https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L24-L27). An attacker can craft a model such that the `size` multiplier is so large that the return value overflows the `int` datatype and becomes negative. In turn, this results in invalid value being given to `malloc`(https://github.com/tensorflow/tensorflow/blob/4ceffae632721e52bf3501b736e4fe9d1221cdfa/tensorflow/lite/c/common.c#L47-L52). In this case, `ret->size` would dereference an invalid pointer. 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 onCreate of EmergencyCallbackModeExitDialog.java, there is a possible way to crash the emergency callback mode due to a missing null check. 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 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).

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

there is a possible persistent Denial of Service due to test/debugging code left in a production build. This could lead to local denial of service of impaired use of the device with no additional execution privileges needed. User interaction is not needed for exploitation.

In multiple functions of ShortcutService.java, there is a possible 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.

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

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

In wifi service, 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 needed

In wifi service, 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 needed

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

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

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

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

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

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

In bt driver, there is a thread competition leads to early release of resources to be accessed. This could lead to local denial of service in kernel.

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

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

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

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

In TeleService, there is a possible system crash due to improper input validation. This could lead to local denial of service with no additional execution privileges needed

In wifi service, 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 needed

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