Tensorflow is an Open Source Machine Learning Framework. A malicious user can cause a denial of service by altering a `SavedModel` such that any binary op would trigger `CHECK` failures. This occurs when the protobuf part corresponding to the tensor arguments is modified such that the `dtype` no longer matches the `dtype` expected by the op. In that case, calling the templated binary operator for the binary op would receive corrupted data, due to the type confusion involved. If `Tin` and `Tout` don't match the type of data in `out` and `input_*` tensors then `flat<*>` would interpret it wrongly. In most cases, this would be a silent failure, but we have noticed scenarios where this results in a `CHECK` crash, hence a denial of service. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. The implementation of `MapStage` is vulnerable a `CHECK`-fail if the key tensor is not a scalar. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `ConcatV2` can be used to trigger a denial of service attack via a segfault caused by a type confusion. The `axis` argument is translated into `concat_dim` in the `ConcatShapeHelper` helper function. Then, a value for `min_rank` is computed based on `concat_dim`. This is then used to validate that the `values` tensor has at least the required rank. However, `WithRankAtLeast` receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that `min_rank` is a 32-bits value and the value of `axis`, the `rank` argument is a negative value, so the error check is bypassed. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

This vulnerability allows local attackers to escalate privileges on affected installations of Linux Kernel 5.14-rc3. An attacker must first obtain the ability to execute low-privileged code on the target system in order to exploit this vulnerability. The specific flaw exists within the handling of eBPF programs. The issue results from the lack of proper validation of user-supplied eBPF programs, which can result in a type confusion condition. An attacker can leverage this vulnerability to escalate privileges and execute arbitrary code in the context of the kernel. Was ZDI-CAN-14689.

Business Logic Errors in GitHub repository pimcore/pimcore prior to 10.2.6.

calibre-web is vulnerable to Business Logic Errors

By passing invalid javascript code where await and yield were called upon non-async and non-generator getter/setter functions, Hermes would invoke generator functions and error out on invalid await/yield positions. This could result in segmentation fault as a consequence of type confusion error, with a low chance of RCE. This issue affects Hermes versions prior to v0.10.0.

Lua v5.4.3 and above are affected by SEGV by type confusion in funcnamefromcode function in ldebug.c which can cause a local denial of service.

There is a Vulnerability of accessing resources using an incompatible type (type confusion) in the MPTCP subsystem in smartphones. Successful exploitation of this vulnerability may cause the system to crash and restart.

The HwNearbyMain module has a Data Processing Errors vulnerability.Successful exploitation of this vulnerability may cause a process to restart.