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.UnsortedSegmentJoin` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `num_segments` is a scalar but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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.LSTMBlockCell` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code does not validate the ranks of any of the arguments to this API call. This results in `CHECK`-failures when the elements of the tensor are accessed. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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

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.Conv3DBackpropFilterV2` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code does not validate that the `filter_sizes` argument is a vector. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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.

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 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 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

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.

fscrypt through v0.3.2 creates a world-writable directory by default when setting up a filesystem, allowing unprivileged users to exhaust filesystem space. We recommend upgrading to fscrypt 0.3.3 or above and adjusting the permissions on existing fscrypt metadata directories where applicable.

The PAM module for fscrypt doesn't adequately validate fscrypt metadata files, allowing users to create malicious metadata files that prevent other users from logging in. A local user can cause a denial of service by creating a fscrypt metadata file that prevents other users from logging into the system. We recommend upgrading to version 0.3.3 or above

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 affected versions of TensorFlow running an LSTM/GRU model where the LSTM/GRU layer receives an input with zero-length results in a CHECK failure when using the CUDA backend. This can result in a query-of-death vulnerability, via denial of service, if users can control the input to the layer. This is fixed in versions 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2, and 2.4.0.

In LocaleList of LocaleList.java, there is a possible forced reboot due to an uncaught exception. This could lead to local denial of service requiring factory reset to restore with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-8.0 Android-8.1 Android-9 Android-10Android ID: A-152410253

In addEscrowToken of LockSettingsService.java, there is a possible loss of the synthetic password due to logic error. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11Android ID: A-168692734

In CellBroadcastReceiver's intent handlers, there is a possible denial of service due to a missing permission check. This could lead to local denial of service of emergency alerts with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-8.0 Android-8.1 Android-9 Android-10 Android-11Android ID: A-162741784

In the settings app, there is a possible app crash due to improper input validation. This could lead to local denial of service of the Settings app with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-10Android ID: A-136005061

In JobStore, there is a mismatched serialization/deserialization for the "battery-not-low" job attribute. This could lead to a local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Product: AndroidVersions: Android-10Android ID: A-130173029

In the Accessibility service, there is a possible permission bypass due to an unsafe PendingIntent. This could lead to local information disclosure with User execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11Android ID: A-154913130

In libstagefright, there is a possible use-after-free due to improper locking. This could lead to local escalation of privilege in the media server with no additional execution privileges needed. User interaction is not needed for exploitation. Product: AndroidVersions: Android-10Android ID: A-77474014

Improper access control vulnerability in KnoxCustomManagerService prior to SMR Jul-2022 Release 1 allows attacker to call PowerManaer.goToSleep method which is protected by system permission by sending braodcast intent.

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).

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.SparseTensorDenseAdd` does not fully validate the input arguments. In this case, a reference gets bound to a `nullptr` during kernel execution. This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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.QuantizedConv2D` does not fully validate the input arguments. In this case, references get bound to `nullptr` for each argument that is empty. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Improper authentication in Link to Windows Service prior to version 2.3.04.1 allows attacker to lock the device. The patch adds proper caller signature check logic.

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.QuantizeAndDequantizeV4Grad` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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 `tf.compat.v1.signal.rfft2d` and `tf.compat.v1.signal.rfft3d` lack input validation and under certain condition can result in crashes (due to `CHECK`-failures). Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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.LoadAndRemapMatrix does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `initializing_values` is a vector but there is no validation for this before accessing its value. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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.GetSessionTensor` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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.UnsortedSegmentJoin` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `num_segments` is a positive scalar but there is no validation. Since this value is used to allocate the output tensor, a negative value would result in a `CHECK`-failure (assertion failure), as per 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 open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, there is a potential for segfault / denial of service in TensorFlow by calling `tf.compat.v1.*` ops which don't yet have support for quantized types, which was added after migration to TensorFlow 2.x. In these scenarios, since the kernel is missing, a `nullptr` value is passed to `ParseDimensionValue` for the `py_value` argument. Then, this is dereferenced, resulting in segfault. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

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.SparseTensorToCSRSparseMatrix` does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack. The code assumes `dense_shape` is a vector and `indices` is a matrix (as part of requirements for sparse tensors) but there is no validation for this. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Stack out-of-bounds write vulnerability in IpcRxImeiUpdateImeiNoti of RILD priro to SMR Jul-2023 Release 1 cause a denial of service on the system.

An use after free vulnerability in sdp driver prior to SMR Mar-2022 Release 1 allows kernel crash.

Improper authentication in One UI Home prior to SMR Mar-2022 Release 1 allows attacker to generate pinned-shortcut without user consent.

Unprotected dynamic receiver in SecSettings prior to SMR Jan-2022 Release 1 allows untrusted applications to launch arbitrary activity.

In ims service, there is a possible unexpected application behavior due to incorrect privilege assignment. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS06219127; Issue ID: ALPS06219127.

In Bluetooth, there is a possible memory corruption due to a logic error. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS06142410; Issue ID: ALPS06142410.

cordova-plugin-fingerprint-aio is a plugin provides a single and simple interface for accessing fingerprint APIs on both Android 6+ and iOS. In versions prior to 5.0.1 The exported activity `de.niklasmerz.cordova.biometric.BiometricActivity` can cause the app to crash. This vulnerability occurred because the activity didn't handle the case where it is requested with invalid or empty data which results in a crash. Any third party app can constantly call this activity with no permission. A 3rd party app/attacker using event listener can continually stop the app from working and make the victim unable to open it. Version 5.0.1 of the cordova-plugin-fingerprint-aio doesn't export the activity anymore and is no longer vulnerable. If you want to fix older versions change the attribute android:exported in plugin.xml to false. Please upgrade to version 5.0.1 as soon as possible.

Improper authorization vulnerability in StorageManagerService prior to SMR Nov-2022 Release 1 allows local attacker to call privileged API.

TensorFlow is an open source platform for machine learning. In affected versions during TensorFlow's Grappler optimizer phase, constant folding might attempt to deep copy a resource tensor. This results in a segfault, as these tensors are supposed to not change. 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.summary.create_file_writer` is called with non-scalar arguments code crashes due to a `CHECK`-fail. 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 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.

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.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `DeserializeSparse` can trigger a null pointer dereference. This is because the shape inference function assumes that the `serialize_sparse` tensor is a tensor with positive rank (and having `3` as the last dimension). 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 the Keras pooling layers can trigger a segfault if the size of the pool is 0 or if a dimension is negative. This is due to the TensorFlow's implementation of pooling operations where the values in the sliding window are not checked to be strictly positive. 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 end-to-end open source platform for machine learning. In affected versions an attacker can craft a TFLite model that would trigger a null pointer dereference, which would result in a crash and denial of service. The [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L268-L285) unconditionally dereferences a pointer. We have patched the issue in GitHub commit 15691e456c7dc9bd6be203b09765b063bf4a380c. 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 trigger a crash via a floating point exception in `tf.raw_ops.ResourceGather`. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/resource_variable_ops.cc#L725-L731) computes the value of a value, `batch_size`, and then divides by it without checking that this value is not 0. We have patched the issue in GitHub commit ac117ee8a8ea57b73d34665cdf00ef3303bc0b11. 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 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.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions an attacker can cause a floating point exception by calling inplace operations with crafted arguments that would result in a division by 0. The [implementation](https://github.com/tensorflow/tensorflow/blob/84d053187cb80d975ef2b9684d4b61981bca0c41/tensorflow/core/kernels/inplace_ops.cc#L283) has a logic error: it should skip processing if `x` and `v` are empty but the code uses `||` instead of `&&`. We have patched the issue in GitHub commit e86605c0a336c088b638da02135ea6f9f6753618. 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.