A memory initialization issue was addressed. This issue is fixed in macOS Big Sur 11.0.1, watchOS 7.1, iOS 12.4.9, watchOS 6.2.9, Security Update 2020-006 High Sierra, Security Update 2020-006 Mojave, iOS 14.2 and iPadOS 14.2, watchOS 5.3.9, macOS Catalina 10.15.7 Supplemental Update, macOS Catalina 10.15.7 Update. A malicious application may be able to disclose kernel memory.

An issue was discovered in kerneld.sys in AIDA64 before 5.99. The vulnerable driver exposes a wrmsr instruction via IOCTL 0x80112084 and does not properly filter the Model Specific Register (MSR). Allowing arbitrary MSR writes can lead to Ring-0 code execution and escalation of privileges.

Improper initialization in the BMC firmware for some Intel(R) Server Boards, Server Systems and Compute Modules before version 2.48.ce3e3bd2 may allow an authenticated user to potentially enable denial of service via local access.

Improper initialization in the firmware for some Intel(R) Processors may allow a privileged user to potentially enable escalation of privilege via local access.

Improper initialization in the firmware for some Intel(R) Processors may allow a privileged user to potentially enable a denial of service via local access.

Improper initialization in a subsystem in the Intel(R) CSME versions before 11.8.86, 11.12.86, 11.22.86, 12.0.81, 13.0.47, 13.30.17, 14.1.53, 14.5.32, 13.50.11 and 15.0.22 may allow a privileged user to potentially enable information disclosure via local access.

A flaw was found in tpm2-tools in versions before 5.1.1 and before 4.3.2. tpm2_import used a fixed AES key for the inner wrapper, potentially allowing a MITM attacker to unwrap the inner portion and reveal the key being imported. The highest threat from this vulnerability is to data confidentiality.

TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `SparseReshape` results in a denial of service based on a `CHECK`-failure. The implementation(https://github.com/tensorflow/tensorflow/blob/e87b51ce05c3eb172065a6ea5f48415854223285/tensorflow/core/kernels/sparse_reshape_op.cc#L40) has no validation that the input arguments specify a valid sparse 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 the only affected versions.

TensorFlow is an end-to-end open source platform for machine learning. The validation in `tf.raw_ops.QuantizeAndDequantizeV2` allows invalid values for `axis` argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L74-L77) uses `||` to mix two different conditions. If `axis_ < -1` the condition in `OP_REQUIRES` will still be true, but this value of `axis_` results in heap underflow. This allows attackers to read/write to other data on the heap. 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 …