TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.ImmutableConst`(https://www.tensorflow.org/api_docs/python/tf/raw_ops/ImmutableConst) with a `dtype` of `tf.resource` or `tf.variant` results in a segfault in the implementation as code assumes that the tensor contents are pure scalars. We have patched the issue in 4f663d4b8f0bec1b48da6fa091a7d29609980fa4 and will release TensorFlow 2.5.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. If using `tf.raw_ops.ImmutableConst` in code, you can prevent the segfault by inserting a filter for the `dtype` argument.

TensorFlow is an end-to-end open source platform for machine learning. Due to lack of validation in `tf.raw_ops.CTCBeamSearchDecoder`, an attacker can trigger denial of service via segmentation faults. The implementation(https://github.com/tensorflow/tensorflow/blob/a74768f8e4efbda4def9f16ee7e13cf3922ac5f7/tensorflow/core/kernels/ctc_decoder_ops.cc#L68-L79) fails to detect cases when the input tensor is empty and proceeds to read data from a null buffer. 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 the `DepthwiseConv` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/1a8e885b864c818198a5b2c0cbbeca5a1e833bc8/tensorflow/lite/kernels/depthwise_conv.cc#L287-L288). An attacker can craft a model such that `input`'s fourth dimension would be 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 `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when calling `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat<T>().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the first line of `png::WriteImageToBuffer`(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. 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.

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.FusedBatchNorm`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/828f346274841fa7505f7020e88ca36c22e557ab/tensorflow/core/kernels/fused_batch_norm_op.cc#L295-L297) performs a division based on the last dimension of the `x` tensor. Since this is controlled by the user, an attacker can trigger 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.

In multiple functions of AppOpsService.java, there is a possible way to saturate the content of /data/system/appops_accesses.xml 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.

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

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

A vulnerability in mfc driver prior to SMR Oct-2021 Release 1 allows memory corruption via NULL-pointer dereference.

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

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

Nullptr dereference when a null char is present in a proto symbol. The symbol is parsed incorrectly, leading to an unchecked call into the proto file's name during generation of the resulting error message. Since the symbol is incorrectly parsed, the file is nullptr. We recommend upgrading to version 3.15.0 or greater.

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 addSubInfo of SubscriptionController.java, there is a possible way to force the user to make a factory reset due to a logic error in the code. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-12Android ID: A-197327688

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause denial of service in applications serving models using `tf.raw_ops.NonMaxSuppressionV5` by triggering a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/image/non_max_suppression_op.cc#L170-L271) uses a user controlled argument to resize a `std::vector`. However, as `std::vector::resize` takes the size argument as a `size_t` and `output_size` is an `int`, there is an implicit conversion to unsigned. If the attacker supplies a negative value, this conversion results in a crash. A similar issue occurs in `CombinedNonMaxSuppression`. We have patched the issue in GitHub commit 3a7362750d5c372420aa8f0caf7bf5b5c3d0f52d and commit [b5cdbf12ffcaaffecf98f22a6be5a64bb96e4f58. 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 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: ALPS05381065.

In SystemSettingsValidators, there is a possible permanent denial of service due to missing bounds checks on UI settings. 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-11Android ID: A-156260178

In RT regmap driver, there is a possible memory corruption due to type confusion. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation. Product: Android; Versions: Android-10, Android-11; Patch ID: ALPS05453809.

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: ALPS05381071.

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 findAllDeAccounts of AccountsDb.java, there is a possible denial of service 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-10 Android-11 Android-12 Android-12L Android-13Android ID: A-169762606

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).

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

In setProfileName of DevicePolicyManagerService.java, there is a possible way to crash the SystemUI menu 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-13Android ID: A-259942964

In ril service, there is a possible out of bounds write due to a missing bounds check. This could lead to local denial of service with System execution privileges needed

The updateMessageStatus function in Android 5.1.1 and earlier allows local users to cause a denial of service (NULL pointer exception and process crash).

TensorFlow is an open source platform for machine learning. In affected versions the process of building the control flow graph for a TensorFlow model is vulnerable to a null pointer exception when nodes that should be paired are not. This occurs because the code assumes that the first node in the pairing (e.g., an `Enter` node) always exists when encountering the second node (e.g., an `Exit` node). When this is not the case, `parent` is `nullptr` so dereferencing it causes a crash. 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 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 write 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 decoder, there is a possible out of bounds write 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 decoder, 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 video decoder, 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 video decoder, there is a possible out of bounds write 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

TensorFlow is an end-to-end open source platform for machine learning. In affected versions providing a negative element to `num_elements` list argument of `tf.raw_ops.TensorListReserve` causes the runtime to abort the process due to reallocating a `std::vector` to have a negative number of elements. The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/list_kernels.cc#L312) calls `std::vector.resize()` with the new size controlled by input given by the user, without checking that this input is valid. We have patched the issue in GitHub commit 8a6e874437670045e6c7dc6154c7412b4a2135e2. 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a division by zero error in LSH [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/lsh_projection.cc#L118). We have patched the issue in GitHub commit 0575b640091680cfb70f4dd93e70658de43b94f9. The fix will be included in TensorFlow 2.6.0. We will also cherrypick thiscommit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

TensorFlow is an end-to-end open source platform for machine learning. Calling `tf.raw_ops.RaggedTensorToVariant` with arguments specifying an invalid ragged tensor results in a null pointer dereference. The implementation of `RaggedTensorToVariant` operations(https://github.com/tensorflow/tensorflow/blob/904b3926ed1c6c70380d5313d282d248a776baa1/tensorflow/core/kernels/ragged_tensor_to_variant_op.cc#L39-L40) does not validate that the ragged tensor argument is non-empty. Since `batched_ragged` contains no elements, `batched_ragged.splits` is a null vector, thus `batched_ragged.splits(0)` will result in dereferencing `nullptr`. 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 null pointer dereference in the implementation of `tf.raw_ops.SparseFillEmptyRows`. This is because of missing validation(https://github.com/tensorflow/tensorflow/blob/fdc82089d206e281c628a93771336bf87863d5e8/tensorflow/core/kernels/sparse_fill_empty_rows_op.cc#L230-L231) that was covered under a `TODO`. If the `dense_shape` tensor is empty, then `dense_shape_t.vec<>()` would cause a null pointer dereference in the implementation of the op. 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. Passing invalid arguments (e.g., discovered via fuzzing) to `tf.raw_ops.SparseCountSparseOutput` results in segfault. 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`-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.

In phasecheckserver, 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 telecom service, there is a possible missing permission check. 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

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