TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. 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. 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.

In memory management driver, there is a possible out of bounds write due to uninitialized data. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android SoCAndroid ID: A-183459083

In AcvpOnMessage of avcp.cpp, there is a possible EOP due to uninitialized data. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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.

In memory management driver, there is a possible out of bounds write due to uninitialized data. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android SoCAndroid ID: A-185196175

In memory management driver, there is a possible out of bounds write due to uninitialized data. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android SoCAndroid ID: A-185195264

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

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

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

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

A security vulnerability has been detected in Rareprob HD Video Player All Formats App 12.1.372 on Android. Impacted is an unknown function of the component com.rocks.music.videoplayer. The manipulation leads to path traversal. The attack needs to be performed locally. The exploit has been disclosed publicly and may be used. The vendor was contacted early about this disclosure but did not respond in any way.

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 the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. 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 code behind `tf.function` API can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive. This occurs due to using a non-reentrant `Lock` Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario. 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 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 read due to improper input validation. This could lead to local denial of service with no additional execution privileges needed

In gpu_pixel_handle_buffer_liveness_update_ioctl of private/google-modules/gpu/mali_kbase/mali_kbase_core_linux.c, there is a possible out of bounds write due to an integer overflow. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

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 phasecheckserver, 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

TensorFlow is an open source platform for machine learning. In affected versions the async implementation of `CollectiveReduceV2` suffers from a memory leak and a use after free. This occurs due to the asynchronous computation and the fact that objects that have been `std::move()`d from are still accessed. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.

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 improper input validation. This could lead to local denial of service with no additional execution privileges needed

In gpu_pixel_handle_buffer_liveness_update_ioctl of private/google-modules/gpu/mali_kbase/platform/pixel/pixel_gpu_slc.c, there is a possible out of bounds write 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.

there is a possible DCK won't be deleted after factory reset 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 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 the APEX module framework of AOSP, there is a possible malicious update to platform components due to improperly used crypto. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation. More details on this can be found in the referenced links.

In CreateAudioBroadcast of broadcaster.cc, 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.

In multiple functions of btm_ble_gap.cc, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege with User execution privileges needed. User interaction is not needed for exploitation.

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 fixUpIncomingShortcutInfo of ShortcutService.java, there is a possible way to view another user's image 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 CreateAudioBroadcast of broadcaster.cc, 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.

In checkKeyIntentParceledCorrectly of AccountManagerService.java, there is a possible way to launch arbitrary activities using system privileges due to Parcel Mismatch. This could lead to local escalation of privilege with no additional execution privileges needed. User interaction is not needed for exploitation.

TensorFlow is an open source platform for machine learning. In affected versions the shape inference function for `Transpose` is vulnerable to a heap buffer overflow. This occurs whenever `perm` contains negative elements. The shape inference function does not validate that the indices in `perm` are all valid. 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 camera service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed

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.

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

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

In telecom service, there is a possible way to write permission usage records of an app due to a missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed

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

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, TensorFlow's `saved_model_cli` tool is vulnerable to a code injection. This can be used to open a reverse shell. This code path was maintained for compatibility reasons as the maintainers had several test cases where numpy expressions were used as arguments. However, given that the tool is always run manually, the impact of this is still not severe. The maintainers have now removed the `safe=False` argument, so all parsing is done without calling `eval`. The patch is available in versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4.

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 several TensorFlow operations are missing validation for the shapes of the tensor arguments involved in the call. Depending on the API, this can result in undefined behavior and segfault or `CHECK`-fail related crashes but in some scenarios writes and reads from heap populated arrays are also possible. We have discovered these issues internally via tooling while working on improving/testing GPU op determinism. As such, we don't have reproducers and there will be multiple fixes for these issues. These fixes will be included in TensorFlow 2.7.0. We will also cherrypick these commits 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 telocom service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed

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

TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing `nullptr`s or via `CHECK`-failures) as well as abuse undefined behavior (binding references to `nullptr`s). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. We will deprecate TensorFlow's boosted trees APIs in subsequent releases. 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 telecom service, there is a possible missing permission check. This could lead to local escalation of privilege with no additional execution privileges needed

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

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