A vulnerability in keras-team/keras version 3.15.0 allows unsafe deserialization of attacker-controlled PyTorch pickle data through the public keras.layers.TorchModuleWrapper.fromconfig method. This method invokes torch.load(..., weightsonly=False) without requiring an explicit unsafe opt-in, such as a safemode=False parameter. When called outside a SafeModeScope(True) context, the absence of an ambient safe mode state permits unsafe deserialization by default. This issue can lead to arbitrary code execution if untrusted Keras layer configurations are processed using this method. The vulnerability arises because the method does not enforce safe deserialization practices unless explicitly guarded by Keras safe mode.

A vulnerability in keras-team/keras version 3.12.0 allows an attacker to craft a malicious tar archive that bypasses the filtersafetarinfos validation in keras/src/utils/fileutils.py. Specifically, symlink entries are not subjected to the same ispathindir validation as regular file entries, allowing symlinks to be created outside the intended extraction directory. This can lead to symlink-based file read, file overwrite, or directory escape attacks. The issue is particularly impactful on Python 3.10 and 3.11, where filtersafetarinfos is the sole defense against tar path traversal. This vulnerability is distinct from CVE-2025-12060 and other previously reported issues.

Arbitrary HDF5 File Read via Virtual Dataset Bypass in keras-team/keras

Keras versions prior to 3.14.0 are vulnerable to a path traversal issue in the archive extraction utilities located in keras/src/utils/fileutils.py. The functions filtersafetarinfos() and filtersafezipinfos() validate archive member paths against the process current working directory (CWD) instead of the actual extraction destination. When the process runs with CWD set to /, which is common in Docker containers, CI/CD runners, and Jupyter environments, the validation boundary becomes the filesystem root, allowing traversal paths to bypass the security check. Additionally, the zip filter contains a bug that causes an AttributeError when a blocked entry is encountered, leading to incomplete extraction. Furthermore, Python 3.11 installations lack the filter="data" safety net, leaving them entirely reliant on the flawed CWD-based filter. Exploitation of this vulnerability can result in arbitrary file writes outside the intended extraction directory, enabling attackers to overwrite configuration files, inject malicious code, or corrupt machine learning datasets and pipelines.