An issue has been found in PowerDNS Authoritative Server before 4.3.1 where an authorized user with the ability to insert crafted records into a zone might be able to leak the content of uninitialized memory.

An information disclosure vulnerability exists in the way that the Microsoft Server Message Block 3.1.1 (SMBv3) protocol handles certain requests, aka 'Windows SMBv3 Client/Server Information Disclosure Vulnerability'.

In rw_t3t_process_error of rw_t3t.cc, there is a possible double free due to uninitialized data. This could lead to remote code execution over NFC with no additional execution privileges needed. User interaction is not needed for exploitation.Product: AndroidVersions: Android-9 Android-10 Android-11 Android-8.1Android ID: A-179687208

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

An improper free of uninitialized memory can occur in DIAG services in Snapdragon Compute, Snapdragon Industrial IOT, Snapdragon Mobile

An information disclosure vulnerability was found in the virtio vhost-user GPU device (vhost-user-gpu) of QEMU in versions up to and including 6.0. The flaw exists in virgl_cmd_get_capset_info() in contrib/vhost-user-gpu/virgl.c and could occur due to the read of uninitialized memory. A malicious guest could exploit this issue to leak memory from the host.

A use of uninitialized value was found in libwebp in versions before 1.0.1 in ReadSymbol().

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.

Exiv2 is a C++ library and a command-line utility to read, write, delete and modify Exif, IPTC, XMP and ICC image metadata. A read of uninitialized memory was found in Exiv2 versions v0.27.3 and earlier. Exiv2 is a command-line utility and C++ library for reading, writing, deleting, and modifying the metadata of image files. The read of uninitialized memory is triggered when Exiv2 is used to read the metadata of a crafted image file. An attacker could potentially exploit the vulnerability to leak a few bytes of stack memory, if they can trick the victim into running Exiv2 on a crafted image file. The bug is fixed in version v0.27.4.