A vulnerability classified as critical has been found in PyTorch 2.6.0. This affects the function torch.jit.script. The manipulation leads to memory corruption. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used.
A vulnerability was found in PyTorch 2.6.0. It has been rated as critical. Affected by this issue is the function torch.nn.utils.rnn.unpack_sequence. The manipulation leads to memory corruption. Attacking locally is a requirement. The exploit has been disclosed to the public and may be used.
A vulnerability was found in PyTorch 2.6.0. It has been declared as critical. Affected by this vulnerability is the function torch.nn.utils.rnn.pad_packed_sequence. The manipulation leads to memory corruption. Local access is required to approach this attack. The exploit has been disclosed to the public and may be used.
A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0+cu124. Affected by this issue is the function torch.mkldnn_max_pool2d. The manipulation leads to denial of service. An attack has to be approached locally. The exploit has been disclosed to the public and may be used. The real existence of this vulnerability is still doubted at the moment. The security policy of the project warns to use unknown models which might establish malicious effects.
Insecure permissions in kuadrant v0.11.3 allow attackers to gain access to the service account's token, leading to escalation of privileges via the secretes component in the k8s cluster
containerd is an open-source container runtime. A bug was found in containerd prior to versions 1.6.38, 1.7.27, and 2.0.4 where containers launched with a User set as a `UID:GID` larger than the maximum 32-bit signed integer can cause an overflow condition where the container ultimately runs as root (UID 0). This could cause unexpected behavior for environments that require containers to run as a non-root user. This bug has been fixed in containerd 1.6.38, 1.7.27, and 2.04. As a workaround, ensure that only trusted images are used and that only trusted users have permissions to import images.
A vulnerability was found in PyTorch 2.6.0+cu124. It has been rated as problematic. Affected by this issue is the function nnq_Sigmoid of the component Quantized Sigmoid Module. The manipulation of the argument scale/zero_point leads to improper initialization. The attack needs to be approached locally. The complexity of an attack is rather high. The exploitation is known to be difficult. The exploit has been disclosed to the public and may be used.
A vulnerability was found in PyTorch 2.6.0+cu124. It has been declared as critical. Affected by this vulnerability is the function torch.ops.profiler._call_end_callbacks_on_jit_fut of the component Tuple Handler. The manipulation of the argument None leads to memory corruption. The attack can be launched remotely. The complexity of an attack is rather high. The exploitation appears to be difficult.
In da, there is a possible out of bounds write due to a missing bounds check. This could lead to local escalation of privilege, if an attacker has physical access to the device, with no additional execution privileges needed. User interaction is needed for exploitation. Patch ID: ALPS09291294; Issue ID: MSV-2061.