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.

In wlan driver, there is a possible missing bounds check. This could lead to local denial of service in wlan services.

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.

In extract3GPPGlobalDescriptions of TextDescriptions.cpp, there is a possible out of bounds read due to an integer overflow. This could lead to local information disclosure from the media server with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-11 Android-12 Android-12LAndroid ID: A-233735886

In nfc_ncif_decode_rf_params of nfc_ncif.cc, there is a possible out of bounds read due to an integer underflow. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-7.1.1 Android-7.1.2 Android-8.0 Android-8.1 Android-9 Android-10Android ID: A-124940143

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

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 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 ProtocolMiscATCommandAdapter::Init() of protocolmiscadapter.cpp, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure with baseband firmware compromise required. 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 Init of protocolembmsadapter.cpp, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure 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 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

In telephone service, there is a possible improper input validation. This could lead to local information disclosure 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 improper input validation. This could lead to local denial of service with no additional execution privileges needed

In SignalStrengthAdapter::FillGsmSignalStrength() of protocolmiscadapter.cpp, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure with baseband firmware compromise required. User interaction is not needed for exploitation.

In GetSizeOfEenlRecords of protocoladapter.cpp, there is a possible out of bounds read due to an incorrect bounds check. This could lead to local information disclosure with no additional execution privileges needed. User interaction is not needed for exploitation.

In Init of protocolnetadapter.cpp, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure 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 validationtools, there is a possible missing permission check. This could lead to local information disclosure with no additional execution privileges needed

In parse_gap_data of utils.cc, there is a possible out of bounds read due to a missing bounds check. This could lead to local information disclosure 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.

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.

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 telephony service, there is a possible missing permission check. This could lead to local information disclosure with no additional execution privileges needed

In telephony service, there is a possible missing permission check. This could lead to local information disclosure with no additional execution privileges needed

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.

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 information disclosure with no additional execution privileges needed

TensorFlow is an open source platform for machine learning. In affected versions TensorFlow's Grappler optimizer has a use of unitialized variable. If the `train_nodes` vector (obtained from the saved model that gets optimized) does not contain a `Dequeue` node, then `dequeue_node` is left unitialized. 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 enginnermode 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 information disclosure 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 dm service, there is a possible missing permission check. This could lead to local information disclosure with no additional execution privileges needed

In engineermode, there is a possible missing permission check. This could lead to local information disclosure with no additional execution privileges needed

In validationtools, there is a possible missing permission check. This could lead to local information disclosure with no additional execution privileges needed

In telephony service, there is a possible missing permission check. This could lead to local information disclosure with no additional execution privileges needed

In validationtools, there is a possible missing permission check. This could lead to local information disclosure with no additional execution privileges needed

In IMS 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 information disclosure 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 validationtools, there is a possible missing permission check. This could lead to local information disclosure with no additional execution privileges needed

There is a possible information disclosure due to a missing permission check. This could lead to local information disclosure of health data with no additional execution privileges needed.

In firewall 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 information disclosure with no additional execution privileges needed

In validationtools, there is a possible missing permission check. This could lead to local information disclosure 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

In omacp 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 information disclosure with no additional execution privileges needed