AI & LLM Vulnerabilities

PyTorch torchvision (a library for computer vision tasks) versions up to 0.28.0 contain an out-of-bounds heap read vulnerability (a bug where software reads memory it shouldn't access) in the GIF image decoder. Attackers can send malicious or broken GIF files to crash programs using this library or steal data from nearby memory.

PyTorch (a Python package for tensor computation) versions before 2.10.0 have a vulnerability in the `weights_only` unpickler that allows attackers to create malicious checkpoint files (.pth files, which store model data) triggering memory corruption and potentially arbitrary code execution (running attacker-chosen commands) when loaded with `torch.load(..., weights_only=True)`. This is a deserialization vulnerability (a weakness where loading untrusted data can be exploited).

CVE-2025-55558 is a buffer overflow (a memory safety error where data is written beyond the intended boundaries) in PyTorch version 2.7.0 that occurs when certain neural network operations are combined and compiled using Inductor, a code compiler. This vulnerability causes a Denial of Service attack (making a service unavailable to users), though no CVSS severity score has been assigned yet.

PyTorch versions before v2.2.0 contain a use-after-free vulnerability (a memory bug where code tries to access data that has already been freed) in the mobile interpreter component. This vulnerability was identified in the torch/csrc/jit/mobile/interpreter.cpp file.