In onNullBinding of RemoteFillService.java, there is a possible background activity launch due to an insecure default value. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In writeInplace of Parcel.cpp, there is a possible out of bounds write. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In multiple locations, there is a possible way to reveal images across users due to improper input validation. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is needed for exploitation.

In checkKeyIntentParceledCorrectly of AccountManagerService.java, there is a possible way to bypass parcel mismatch mitigation due to unsafe deserialization. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is needed for exploitation.

In multiple locations, there is a possible way to leak audio files across user profiles due to a confused deputy. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In many functions of ComputerEngine.java, there is a possible way to access URIs across users due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In FrpBypassAlertActivity of FrpBypassAlertActivity.java, there is a possible way to bypass FRP due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In multiple functions of Permissions.java, there is a possible way to override the state of the user's location permissions due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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

In multiple functions of CompanionDeviceManagerService.java, there is a possible way to grant permissions without user consent due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In applyTaskFragmentOperation of WindowOrganizerController.java, there is a possible way to launch arbitrary activities as the system UID due to a logic error in the code. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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

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

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

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

In FuseDaemon.cpp, there is a possible out of bounds write due to memory corruption. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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

In avrc_vendor_msg of avrc_opt.cc, there is a possible out of bounds write due to a heap buffer overflow. This could lead to paired device escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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

In PackageInstaller.Session#transfer of frameworks/base/services/core/java/com/android/server/pm/PackageInstallerSession.java, there is a possible memory exhaustion attack due to a logic error in the code. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

NVIDIA Linux kernel distributions contain a vulnerability in nvmap NVGPU_IOCTL_CHANNEL_SET_ERROR_NOTIFIER, where improper access control may lead to code execution, compromised integrity, or denial of service.

In addWindow of WindowManagerService.java, there is a possible tapjacking issue due to a tapjacking/overlay attack. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In AndroidManifest.xml, there is a possible persistent denial of service due to a missing permission check. This could lead to local denial of service with no additional execution privileges needed. User interaction is not needed for exploitation.

Intent redirection in Samsung Experience Service versions 10.8.0.4 in Android P(9.0) below, and 12.2.0.5 in Android Q(10.0) above allows attacker to execute privileged action.

Improper input validation for some Intel Unison software may allow an authenticated user to potentially enable escalation of privilege via local access.

Using unsafe PendingIntent in Customization Service prior to version 2.2.02.1 in Android O(8.x), 2.4.03.0 in Android P(9.0), 2.7.02.1 in Android Q(10.0) and 2.9.01.1 in Android R(11.0) allows local attackers to perform unauthorized action without permission via hijacking the PendingIntent.

An improper length check in APAService prior to SMR Sep-2021 Release 1 results in stack based Buffer Overflow.

An improper exception control in softsimd prior to SMR APR-2021 Release 1 allows unprivileged applications to access the API in softsimd.

An improper validation vulnerability in FilterProvider prior to SMR Dec-2021 Release 1 allows attackers to write arbitrary files via a path traversal vulnerability.

An improper validation vulnerability in FilterProvider prior to SMR Dec-2021 Release 1 allows local arbitrary code execution.

Improper validation check vulnerability in PackageManager prior to SMR July-2021 Release 1 allows untrusted applications to get dangerous level permission without user confirmation in limited circumstances.

Using unsafe PendingIntent in Samsung Account in versions 10.8.0.4 in Android P(9.0) and below, and 12.1.1.3 in Android Q(10.0) and above allows local attackers to perform unauthorized action without permission via hijacking the PendingIntent.

A possible out of bounds write vulnerability in NPU driver prior to SMR JUN-2021 Release 1 allows arbitrary memory write.

An improper input validation vulnerability in LDFW prior to SMR Dec-2021 Release 1 allows attackers to perform arbitrary code execution.

Improper sanitization of incoming intent in Samsung Contacts prior to SMR JUN-2021 Release 1 allows local attackers to copy or overwrite arbitrary files with Samsung Contacts privilege.

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/31bd5026304677faa8a0b77602c6154171b9aec1/tensorflow/core/kernels/image/draw_bounding_box_op.cc#L116-L130) assumes that the last element of `boxes` input is 4, as required by [the op](https://www.tensorflow.org/api_docs/python/tf/raw_ops/DrawBoundingBoxesV2). Since this is not checked attackers passing values less than 4 can write outside of bounds of heap allocated objects and cause memory corruption. If the last dimension in `boxes` is less than 4, accesses similar to `tboxes(b, bb, 3)` will access data outside of bounds. Further during code execution there are also writes to these indices. 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.MaxPool3DGradGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L694-L696) does not check that the initialization of `Pool3dParameters` completes successfully. Since the constructor(https://github.com/tensorflow/tensorflow/blob/596c05a159b6fbb9e39ca10b3f7753b7244fa1e9/tensorflow/core/kernels/pooling_ops_3d.cc#L48-L88) uses `OP_REQUIRES` to validate conditions, the first assertion that fails interrupts the initialization of `params`, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values. 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. If the `splits` argument of `RaggedBincount` does not specify a valid `SparseTensor`(https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor), then an attacker can trigger a heap buffer overflow. This will cause a read from outside the bounds of the `splits` tensor buffer in the implementation of the `RaggedBincount` op(https://github.com/tensorflow/tensorflow/blob/8b677d79167799f71c42fd3fa074476e0295413a/tensorflow/core/kernels/bincount_op.cc#L430-L446). Before the `for` loop, `batch_idx` is set to 0. The attacker sets `splits(0)` to be 7, hence the `while` loop does not execute and `batch_idx` remains 0. This then results in writing to `out(-1, bin)`, which is before the heap allocated buffer for the output tensor. 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.

TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseAdd` results in allowing attackers to exploit undefined behavior (dereferencing null pointers) as well as write outside of bounds of heap allocated data. The implementation(https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/sparse_sparse_binary_op_shared.cc) has a large set of validation for the two sparse tensor inputs (6 tensors in total), but does not validate that the tensors are not empty or that the second dimension of `*_indices` matches the size of corresponding `*_shape`. This allows attackers to send tensor triples that represent invalid sparse tensors to abuse code assumptions that are not protected by validation. 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.

A possible buffer overflow vulnerability in NPU driver prior to SMR JUN-2021 Release 1 allows arbitrary memory write and code execution.

Using unsafe PendingIntent in Slow Motion Editor prior to version 3.5.18.5 allows local attackers unauthorized action without permission via hijacking the PendingIntent.

TensorFlow is an end-to-end open source platform for machine learning. The optimized implementation of the `TransposeConv` TFLite operator is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/0d45ea1ca641b21b73bcf9c00e0179cda284e7e7/tensorflow/lite/kernels/internal/optimized/optimized_ops.h#L5221-L5222). An attacker can craft a model such that `stride_{h,w}` values are 0. Code calling this function must validate these arguments. 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.AvgPool3DGrad` is vulnerable to a heap buffer overflow. The implementation(https://github.com/tensorflow/tensorflow/blob/d80ffba9702dc19d1fac74fc4b766b3fa1ee976b/tensorflow/core/kernels/pooling_ops_3d.cc#L376-L450) assumes that the `orig_input_shape` and `grad` tensors have similar first and last dimensions but does not check that this assumption is validated. 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 `SVDF` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/7f283ff806b2031f407db64c4d3edcda8fb9f9f5/tensorflow/lite/kernels/svdf.cc#L99-L102). An attacker can craft a model such that `params->rank` 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. The `Prepare` step of the `SpaceToDepth` TFLite operator does not check for 0 before division(https://github.com/tensorflow/tensorflow/blob/5f7975d09eac0f10ed8a17dbb6f5964977725adc/tensorflow/lite/kernels/space_to_depth.cc#L63-L67). An attacker can craft a model such that `params->block_size` would be zero. 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 `EmbeddingLookup` TFLite operator is vulnerable to a division by zero error(https://github.com/tensorflow/tensorflow/blob/e4b29809543b250bc9b19678ec4776299dd569ba/tensorflow/lite/kernels/embedding_lookup.cc#L73-L74). An attacker can craft a model such that the first dimension of the `value` input is 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. The implementation of `tf.raw_ops.MaxPool3DGradGrad` exhibits undefined behavior by dereferencing null pointers backing attacker-supplied empty tensors. The implementation(https://github.com/tensorflow/tensorflow/blob/72fe792967e7fd25234342068806707bbc116618/tensorflow/core/kernels/pooling_ops_3d.cc#L679-L703) fails to validate that the 3 tensor inputs are not empty. If any of them is empty, then accessing the elements in the tensor results in dereferencing a null 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.

TensorFlow is an end-to-end open source platform for machine learning. The fix for CVE-2020-15209(https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-15209) missed the case when the target shape of `Reshape` operator is given by the elements of a 1-D tensor. As such, the fix for the vulnerability(https://github.com/tensorflow/tensorflow/blob/9c1dc920d8ffb4893d6c9d27d1f039607b326743/tensorflow/lite/core/subgraph.cc#L1062-L1074) allowed passing a null-buffer-backed tensor with a 1D shape. 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.io.decode_raw` produces incorrect results and crashes the Python interpreter when combining `fixed_length` and wider datatypes. The implementation of the padded version(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc) is buggy due to a confusion about pointer arithmetic rules. First, the code computes(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L61) the width of each output element by dividing the `fixed_length` value to the size of the type argument. The `fixed_length` argument is also used to determine the size needed for the output tensor(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L63-L79). This is followed by reencoding code(https://github.com/tensorflow/tensorflow/blob/1d8903e5b167ed0432077a3db6e462daf781d1fe/tensorflow/core/kernels/decode_padded_raw_op.cc#L85-L94). The erroneous code is the last line above: it is moving the `out_data` pointer by `fixed_length * sizeof(T)` bytes whereas it only copied at most `fixed_length` bytes from the input. This results in parts of the input not being decoded into the output. Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory. 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. A specially crafted TFLite model could trigger an OOB write on heap in the TFLite implementation of `ArgMin`/`ArgMax`(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/arg_min_max.cc#L52-L59). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the condition in the `if` is never true, so code writes past the last valid element of `output_dims->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.