Adobe After Effects version 18.2.1 (and earlier) is affected by an Improper input validation vulnerability when parsing a specially crafted file. An unauthenticated attacker could leverage this vulnerability to disclose arbitrary memory information in the context of the current user. Exploitation of this issue requires user interaction in that a victim must open a malicious file.

Due to an Improper Initialization vulnerability in Juniper Networks Junos OS on PTX platforms and QFX10K Series with Paradise (PE) chipset-based line cards, ddos-protection configuration changes made from the CLI will not take effect as expected beyond the default DDoS (Distributed Denial of Service) settings in the Packet Forwarding Engine (PFE). This may cause BFD sessions to flap when a high rate of specific packets are received. Flapping of BFD sessions in turn may impact routing protocols and network stability, leading to a Denial of Service (DoS) condition. Continued receipt and processing of this packet will create a sustained Denial of Service (DoS) condition. This issue affects only the following platforms with Paradise (PE) chipset-based line cards: PTX1000, PTX3000 (NextGen), PTX5000, PTX10008, PTX10016 Series and QFX10002 Series. This issue affects: Juniper Networks Junos OS 17.4 versions prior to 17.4R3-S5 on PTX Series, QFX10K Series; 18.2 versions prior to 18.2R3-S8 on …

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 …