In apusys driver, there is a possible system crash due to an integer overflow. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS06479562; Issue ID: ALPS06479562.
CWE-190
CVE-2022-21761
In apusys driver, there is a possible system crash due to an integer overflow. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS06479532; Issue ID: ALPS06479532.
CVE-2022-21762
In apusys driver, there is a possible system crash due to an integer overflow. This could lead to local denial of service with System execution privileges needed. User interaction is not needed for exploitation. Patch ID: ALPS06477946; Issue ID: ALPS06477946.
CVE-2022-21801
A denial of service vulnerability exists in the netserver recv_command functionality of reolink RLC-410W v3.0.0.136_20121102. A specially-crafted network request can lead to a reboot. An attacker can send a malicious packet to trigger this vulnerability.
CVE-2022-21821
NVIDIA CUDA Toolkit SDK contains an integer overflow vulnerability in cuobjdump.To exploit this vulnerability, a remote attacker would require a local user to download a specially crafted, corrupted file and locally execute cuobjdump against the file. Such an attack may lead to remote code execution that causes complete denial of service and an impact on data confidentiality and integrity.
CVE-2022-21727
Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. 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.