Threat Intelligence Database

Keras versions up to 3.15.0 contain a vulnerability in the keras.models.load_model() function that allows denial of service (DoS) via loading specially crafted .keras model files. The flaw arises from lack of validation on dataset shape or size in H5IOStore.__getitem__, leading to unbounded memory allocation and potential out-of-memory termination. This vulnerability bypasses a previous fix for a related issue (CVE-2026-0897). The attack vector includes poisoned models from untrusted sources, posing a risk to machine learning pipelines that load external models.

Keras versions up to 3.15.0 contain a vulnerability that allows denial of service (DoS) via loading malicious .keras model files. The issue arises from unvalidated dataset shapes and sizes during model loading, causing unbounded memory allocation and potential out-of-memory crashes. This vulnerability bypasses a previous fix and affects machine learning pipelines processing untrusted models.

A vulnerability in keras-team/keras versions <= 3.14.0 allows arbitrary local HDF5 file content disclosure due to improper handling of HDF5 ExternalLinks. The `KerasFileEditor` and `keras.saving.load_weights` functions bypass the `safe_get_h5_group` and `safe_get_h5_dataset` helpers, which are designed to reject ExternalLinks and SoftLinks. This results in automatic dereferencing of links to external HDF5 files, enabling attackers to disclose sensitive data from the victim's local filesystem. Specifically, `KerasFileEditor` extracts attributes and datasets from linked files into its internal structures, while `keras.saving.load_weights` loads weights from linked files into the user's model. This issue can be exploited by providing a malicious `.h5`, `.weights.h5`, or `.keras` file containing ExternalLinks.

Keras versions before 3.12.2 and between 3.12.3 and before 3.14.1 contain a vulnerability that allows an attacker to craft malicious model files with Virtual Datasets referencing external files. Loading such models can cause unintended reading of external files, potentially disclosing sensitive information. This issue is due to incomplete validation of the dataset's virtual property. The vulnerability is fixed in versions 3.12.2 and 3.14.1.

Keras versions prior to 3.14.0 are vulnerable to a path traversal issue in the archive extraction utilities located in `keras/src/utils/file_utils.py`. The functions `filter_safe_tarinfos()` and `filter_safe_zipinfos()` 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.

A vulnerability in the `TFSMLayer` class of the `keras` package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of `.keras` models, even when `safe_mode=True`. This bypasses the security guarantees of `safe_mode` and enables arbitrary attacker-controlled code execution during model inference under the victim's privileges. The issue arises due to the unconditional loading of external SavedModels, serialization of attacker-controlled file paths, and the lack of validation in the `from_config()` method.

Allocation of Resources Without Limits or Throttling in the HDF5 weight loading component in Google Keras 3.0.0 through 3.13.0 on all platforms allows a remote attacker to cause a Denial of Service (DoS) through memory exhaustion and a crash of the Python interpreter via a crafted .keras archive containing a valid model.weights.h5 file whose dataset declares an extremely large shape.

Keras version 3.11.3 is affected by a path traversal vulnerability in the keras.utils.get_file() function when extracting tar archives. The vulnerability arises because the function uses Python's tarfile.extractall() method without the security-critical filter='data' parameter. Although Keras attempts to filter unsafe paths using filter_safe_paths(), this filtering occurs before extraction, and a PATH_MAX symlink resolution bug triggers during extraction. This bug causes symlink resolution to fail due to path length limits, resulting in a security bypass that allows files to be written outside the intended extraction directory. This can lead to arbitrary file writes outside the cache directory, enabling potential system compromise or malicious code execution. The vulnerability affects Keras installations that process tar archives with get_file() and does not affect versions where this extraction method is secured with the appropriate filter parameter.

HighVulnerabilityGermanyFranceUnited Kingdom+4#cve#cve-2025-12638#cwe-22

The Keras Model.load_model function permits arbitrary code execution, even with safe_mode=True, through a manually constructed, malicious .keras archive. By altering the config.json file within the archive, an attacker can specify arbitrary Python modules and functions, along with their arguments, to be loaded and executed during model loading.

HighVulnerabilityGermanyFranceUnited Kingdom+5#cve#cve-2025-1550#cwe-94

An issue in keras 3.7.0 allows attackers to write arbitrary files to the user's machine via downloading a crafted tar file through the get_file function.

MediumVulnerabilityUnited StatesChinaIndia+7#cve#cve-2024-55459