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 …

TensorFlow is an end-to-end open source platform for machine learning. Incomplete validation in `tf.raw_ops.CTCLoss` allows an attacker to trigger an OOB read from heap. The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits 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. 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_add_op.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.

Exim 4 before 4.94.2 has Improper Initialization that can lead to recursion-based stack consumption or other consequences. This occurs because use of certain getc functions is mishandled when a client uses BDAT instead of DATA.

Due to an improper Initialization vulnerability on Juniper Networks Junos OS QFX5100-96S devices with QFX 5e Series image installed, ddos-protection configuration changes will not take effect beyond the default DDoS (Distributed Denial of Service) settings when configured from the CLI. The DDoS protection (jddosd) daemon allows the device to continue to function while protecting the packet forwarding engine (PFE) during the DDoS attack. When this issue occurs, the default DDoS settings within the PFE apply, as CPU bound packets will be throttled and dropped in the PFE when the limits are exceeded. To check if the device has this issue, the administrator can execute the following command to monitor the status of DDoS protection: user@device> show ddos-protection protocols error: the ddos-protection subsystem is not running This issue affects only QFX5100-96S devices. No other products or platforms are affected by this issue. This issue affects: Juniper Networks Junos OS on QFX5100-96S:…

On Juniper Networks Junos OS Evolved devices, receipt of a specific IPv6 packet may cause an established IPv6 BGP session to terminate, creating a Denial of Service (DoS) condition. Continued receipt and processing of this packet will create a sustained Denial of Service (DoS) condition. This issue does not affect IPv4 BGP sessions. This issue affects IBGP or EBGP peer sessions with IPv6. This issue affects: Juniper Networks Junos OS Evolved: 19.4 versions prior to 19.4R2-S3-EVO; 20.1 versions prior to 20.1R2-S3-EVO; 20.2 versions prior to 20.2R2-S1-EVO; 20.3 versions prior to 20.3R2-EVO. This issue does not affect Juniper Networks Junos OS releases.