Fuji Electric V-Server Lite and Tellus Lite V-Simulator prior to v4.0.12.0 is vulnerable to an access of uninitialized pointer, which may allow an attacker read from or write to unexpected memory locations, leading to a denial-of-service.

Adobe Premiere Rush versions 1.5.16 (and earlier) allows access to an uninitialized pointer vulnerability that allows remote attackers to disclose arbitrary data on affected installations. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of MP4 files. The issue results from the lack of proper initialization of memory prior to accessing it.

Adobe Premiere Rush versions 1.5.16 (and earlier) allows access to an uninitialized pointer vulnerability that allows remote attackers to disclose sensitive information on affected installations. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of MP4 files. The issue results from the lack of proper initialization of memory prior to accessing it.

TensorFlow is an open source platform for machine learning. In affected versions the code for boosted trees in TensorFlow is still missing validation. As a result, attackers can trigger denial of service (via dereferencing `nullptr`s or via `CHECK`-failures) as well as abuse undefined behavior (binding references to `nullptr`s). An attacker can also read and write from heap buffers, depending on the API that gets used and the arguments that are passed to the call. Given that the boosted trees implementation in TensorFlow is unmaintained, it is recommend to no longer use these APIs. We will deprecate TensorFlow's boosted trees APIs in subsequent releases. 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 `tf.ragged.cross` has an undefined behavior due to binding a reference to `nullptr`. 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 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.

TensorFlow is an open source platform for machine learning. In affected versions the code for sparse matrix multiplication is vulnerable to undefined behavior via binding a reference to `nullptr`. This occurs whenever the dimensions of `a` or `b` are 0 or less. In the case on one of these is 0, an empty output tensor should be allocated (to conserve the invariant that output tensors are always allocated when the operation is successful) but nothing should be written to it (that is, we should return early from the kernel implementation). Otherwise, attempts to write to this empty tensor would result in heap OOB access. 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 affeced versions during execution, `EinsumHelper::ParseEquation()` is supposed to set the flags in `input_has_ellipsis` vector and `*output_has_ellipsis` boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to `true` and never assigns `false`. This results in unitialized variable access if callers assume that `EinsumHelper::ParseEquation()` always sets these flags. 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.

A crafted request may cause a read access to an uninitialized pointer in CODESYS V2 Runtime Toolkit 32 Bit full and PLCWinNT prior to versions V2.4.7.56, resulting in a denial-of-service condition.

A vulnerability has been identified in NX 1953 Series (All versions < V1973.3700), NX 1980 Series (All versions < V1988), Solid Edge SE2021 (All versions < SE2021MP8). The affected application is vulnerable to information disclosure by unexpected access to an uninitialized pointer while parsing user-supplied OBJ files. An attacker could leverage this vulnerability to leak information from unexpected memory locations (ZDI-CAN-13770).