In firewall service, there is a missing permission check. This could lead to local escalation of privilege with system execution privileges needed.

In messaging service, there is a missing permission check. This could lead to elevation of privilege in contacts service with no additional execution privileges needed.

In setupVideoEncoder of StagefrightRecorder.cpp, there is a possible asynchronous playback when B-frame support is enabled. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

In power management service, there is a missing permission check. This could lead to set up power management service with no additional execution privileges needed.

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.

In PVRSRV_MMap of pvr_bridge_k.c, there is a possible arbitrary code execution due to a logic error in the code. This could lead to local escalation of privilege in the kernel with no additional execution privileges needed. User interaction is not needed for exploitation.

In trusty_ffa_mem_reclaim of shared-mem-smcall.c, there is a possible privilege escalation due to improper input validation. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android kernelAndroid ID: A-237838301References: N/A

TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation for `tf.raw_ops.ExperimentalDatasetToTFRecord` and `tf.raw_ops.DatasetToTFRecord` can trigger heap buffer overflow and segmentation fault. The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/data/experimental/to_tf_record_op.cc#L93-L102) assumes that all records in the dataset are of string type. However, there is no check for that, and the example given above uses numeric types. We have patched the issue in GitHub commit e0b6e58c328059829c3eb968136f17aa72b6c876. 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 getView of AddAppNetworksFragment.java, there is a possible way to mislead the user about network add requests due to improper input validation. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-13Android ID: A-224545390

In power management service, there is a missing permission check. This could lead to set up power management service with no additional execution privileges needed.

TensorFlow is an end-to-end open source platform for machine learning. In affected versions due to incomplete validation in MKL implementation of requantization, an attacker can trigger undefined behavior via binding a reference to a null pointer or can access data outside the bounds of heap allocated arrays. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantization_range_per_channel_op.cc) does not validate the dimensions of the `input` tensor. A similar issue occurs in `MklRequantizePerChannelOp`. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/core/kernels/mkl/mkl_requantize_per_channel_op.cc) does not perform full validation for all the input arguments. We have patched the issue in GitHub commit 9e62869465573cb2d9b5053f1fa02a81fce21d69 and in the Github commit 203214568f5bc237603dbab6e1fd389f1572f5c9. 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 writeToParcel and createFromParcel of DcParamObject.java, there is a permission bypass due to a write size mismatch. This could lead to an elevation of privileges where the user can start an activity with system privileges, with no additional execution privileges needed. User interaction is not needed for exploitation.

In String16 of String16.cpp, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege in an unprivileged process with no additional execution privileges needed. User interaction is not needed for exploitation.

In createFromParcel of ViewPager.java, there is a possible read/write serialization issue leading to a permissions bypass. This could lead to local escalation of privilege where an app can start an activity with system privileges with no additional execution privileges needed. User interaction is not needed for exploitation.

In checkPermissions of RecognitionService.java, there is a possible permissions bypass 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 setTransactionState of SurfaceFlinger.cpp, there is a possible way to perform tapjacking 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.

Improper protection in IOMMU prior to SMR Oct-2022 Release 1 allows unauthorized access to secure memory.

A heap-based overflow vulnerability in makeContactAGIF in libagifencoder.quram.so library prior to SMR Oct-2022 Release 1 allows attacker to perform code execution.

Heap overflow vulnerability in sflacf_fal_bytes_peek function in libsmat.so library prior to SMR Nov-2022 Release 1 allows local attacker to execute arbitrary code.

Integer overflow vulnerability in Samsung decoding library for video thumbnails prior to SMR Dec-2022 Release 1 allows local attacker to perform Out-Of-Bounds Write.

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

In createFromParcel of MediaCas.java, there is a possible parcel read/write mismatch due to improper input validation. This could lead to local escalation of privilege where an app can start an activity with system privileges with no additional execution privileges needed. User interaction is not needed for exploitation.

In setAllowOnlyVpnForUids of NetworkManagementService.java, there is a possible security settings bypass due to a missing permission check. This could lead to local escalation of privilege allowing users to access non-VPN networks, when they are supposed to be restricted to the VPN networks, with no additional execution privileges needed. User interaction is not needed for exploitation.

Invalid memory access in Sentencepiece versions less than 0.2.1 when using a vulnerable model file, which is not created in the normal training procedure.

A use after free vulnerability in perf-mgr driver prior to SMR Oct-2022 Release 1 allows attacker to cause memory access fault.

Improper input validation vulnerability in DualOutFocusViewer prior to SMR Nov-2022 Release 1 allows local attacker to perform an arbitrary code execution.

In the read() function of ProcessStats.java, there is a possible read/write serialization issue leading to a permissions bypass. This could lead to local escalation of privilege where an app can start an activity with system privileges with no additional execution privileges needed. User interaction is not needed for exploitation.

In power management service, there is a missing permission check. This could lead to set up power management service with no additional execution privileges needed.

In soundrecorder service, there is a missing permission check. This could lead to elevation of privilege in contacts service with no additional execution privileges needed.

In power management service, there is a missing permission check. This could lead to set up power management service with no additional execution privileges needed.

In messaging service, there is a missing permission check. This could lead to elevation of privilege in contacts service with no additional execution privileges needed.

TensorFlow is an end-to-end open source platform for machine learning. A specially crafted TFLite model could trigger an OOB read on heap in the TFLite implementation of `Split_V`(https://github.com/tensorflow/tensorflow/blob/c59c37e7b2d563967da813fa50fe20b21f4da683/tensorflow/lite/kernels/split_v.cc#L99). If `axis_value` is not a value between 0 and `NumDimensions(input)`, then the `SizeOfDimension` function(https://github.com/tensorflow/tensorflow/blob/102b211d892f3abc14f845a72047809b39cc65ab/tensorflow/lite/kernels/kernel_util.h#L148-L150) will access data outside the bounds of the tensor shape array. 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 Music service, there is a missing permission check. This could lead to elevation of privilege in Music service with no additional execution privileges needed.

In power management service, there is a missing permission check. This could lead to set up power management service with no additional execution privileges needed.

In power management service, there is a missing permission check. This could lead to set up power management service with no additional execution privileges needed.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger undefined behavior by binding to null pointer in `tf.raw_ops.ParameterizedTruncatedNormal`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/3f6fe4dfef6f57e768260b48166c27d148f3015f/tensorflow/core/kernels/parameterized_truncated_normal_op.cc#L630) does not validate input arguments before accessing the first element of `shape`. If `shape` argument is empty, then `shape_tensor.flat<T>()` is an empty array. 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 Music service, there is a missing permission check. This could lead to elevation of privilege in Music service with no additional execution privileges needed.

In CryptoPlugin::decrypt of CryptoPlugin.cpp, there is a possible out of bounds write due to stale pointer. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-8.0 Android-8.1 Android-9 Android-10Android ID: A-144351324

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

In Music service, there is a missing permission check. This could lead to elevation of privilege in Music service with no additional execution privileges needed.

In power management service, there is a missing permission check. This could lead to set up power management service with no additional execution privileges needed.

In power management service, there is a missing permission check. This could lead to set up power management service with no additional execution privileges needed.

In power management service, there is a missing permission check. This could lead to set up power management service with no additional execution privileges needed.

In power management service, there is a missing permission check. This could lead to set up power management service with no additional execution privileges needed.

In Music service, there is a missing permission check. This could lead to elevation of privilege in Music service with no additional execution privileges needed.

In soundrecorder service, there is a missing permission check. This could lead to elevation of privilege in contacts service with no additional execution privileges needed.

In Soundrecorder service, there is a missing permission check. This could lead to elevation of privilege in Soundrecorder service with no additional execution privileges needed.

In power management service, there is a missing permission check. This could lead to set up power management service with no additional execution privileges needed.

In power management service, there is a missing permission check. This could lead to set up power management service with no additional execution privileges needed.

In gps, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS07573237; Issue ID: ALPS07573237.