Advisories for Pypi/Keras package

A vulnerability in keras-team/keras versions < 3.15.0 allows for a denial of service (DoS) attack when loading malicious .keras model files via the keras.models.load_model() function. The H5IOStore.getitem method in keras/src/saving/saving_lib.py does not validate the shape or size of datasets, leading to unbounded memory allocation. A specially crafted .keras file can exploit this flaw to trigger an out-of-memory (OOM) condition, causing the process to be terminated (exit code 137). This issue …

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 …

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.from_config method. This method invokes torch.load(…, weights_only=False) without requiring an explicit unsafe opt-in, such as a safe_mode=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 …

A vulnerability in keras-team/keras version 3.12.0 allows an attacker to craft a malicious tar archive that bypasses the filter_safe_tarinfos validation in keras/src/utils/file_utils.py. Specifically, symlink entries are not subjected to the same is_path_in_dir 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, …

A vulnerability in keras-team/keras version 3.14.0 allows for arbitrary code execution due to improper handling of deserialization in the Lambda layer. Specifically, the _raise_for_lambda_deserialization() function fails to enforce the safe-mode guard when safe_mode is set to None, which is the default value when from_config() is called outside of a SafeModeScope context. This logic error conflates None (unset/default-deny) with False (explicitly disabled), bypassing the guard and allowing attacker-controlled marshal bytecode to …

Keras versions up to and including 3.13.2 are vulnerable to an arbitrary HDF5 file read due to an incomplete fix for CVE-2026-1669. The vulnerability resides in the H5IOStore._verify_dataset() and file_editor.py methods, which fail to check the dataset.is_virtual property of HDF5 datasets. This allows an attacker to craft a malicious .keras model archive or .h5 weights file containing a Virtual Dataset (VDS) that references external HDF5 files on the victim's filesystem. …

A path traversal vulnerability exists in keras-team/keras version 3.14.0, specifically in the DiskIOStore.make method within the Keras 3 model saving and loading library. This vulnerability arises from the improper handling of user-provided layer names, which are used to construct directory paths without sanitizing for parent directory components (..). While forward slashes (/) are restricted in layer names, directory traversal sequences are not. This allows an attacker to craft a malicious …

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 …

Keras’s model loader (KerasFileEditor) unsafely loads user-supplied .keras model files containing HDF5-based weight files without performing any validation on HDF5 dataset metadata. An attacker can craft a .keras archive containing a valid model.weights.h5 file whose dataset declares an extremely large shape (e.g. (50_000_000, 50_000_000)), but stores only a few bytes. The .keras file remains small (100–400 KB) because HDF5 with gzip compression stores minimal data. During model loading, Keras executes: …

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 …

TensorFlow / Keras continues to honor HDF5 “external storage” and ExternalLink features when loading weights. A malicious .weights.h5 (or a .keras archive embedding such weights) can direct load_weights() to read from an arbitrary readable filesystem path. The bytes pulled from that path populate model tensors and become observable through inference or subsequent re-save operations. Keras “safe mode” only guards object deserialization and does not cover weight I/O, so this behaviour …

Duplicate Advisory This advisory has been withdrawn because it is a duplicate of GHSA-3m4q-jmj6-r34q. This link is maintained to preserve external references. Original Description Arbitrary file read in the model loading mechanism (HDF5 integration) in Keras versions 3.0.0 through 3.13.1 on all supported platforms allows a remote attacker to read local files and disclose sensitive information via a crafted .keras model file utilizing HDF5 external dataset references.

This advisory has been withdrawn.

Keras's keras.utils.get_file() function is vulnerable to directory traversal attacks despite implementing filter_safe_paths(). The vulnerability exists because extract_archive() uses Python's tarfile.extractall() method without the security-critical filter="data" parameter. A PATH_MAX symlink resolution bug occurs before path filtering, allowing malicious tar archives to bypass security checks and write files outside the intended extraction directory.

Duplicate Advisory This advisory has been withdrawn because it is a duplicate of GHSA-hjqc-jx6g-rwp9. This link is maintained to preserve external references. Original Description 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 …

Duplicate Advisory This advisory has been withdrawn because it is a duplicate of GHSA-hjqc-jx6g-rwp9. This link is maintained to preserve external references. Original Description The keras.utils.get_file API in Keras, when used with the extract=True option for tar archives, is vulnerable to a path traversal attack. The utility uses Python's tarfile.extractall function without the filter="data" feature. A remote attacker can craft a malicious tar archive containing special symlinks, which, when extracted, …

The Keras.Model.load_model method, including when executed with the intended security mitigation safe_mode=True, is vulnerable to arbitrary local file loading and Server-Side Request Forgery (SSRF). This vulnerability stems from the way the StringLookup layer is handled during model loading from a specially crafted .keras archive. The constructor for the StringLookup layer accepts a vocabulary argument that can specify a local file path or a remote file path. Arbitrary Local File Read: …

Deserialization of untrusted data can occur in versions of the Keras framework running versions 3.11.0 up to but not including 3.11.3, enabling a maliciously uploaded Keras file containing a TorchModuleWrapper class to run arbitrary code on an end user’s system when loaded despite safe mode being enabled. The vulnerability can be triggered through both local and remote files.

When a model in the .h5 (or .hdf5) format is loaded using the Keras Model.load_model method, the safe_mode=True setting is silently ignored without any warning or error. This allows an attacker to execute arbitrary code on the victim’s machine with the same privileges as the Keras application. This report is specific to the .h5/.hdf5 file format. The attack works regardless of the other parameters passed to load_model and does not …

Arbitrary Code Execution in Keras Keras versions prior to 3.11.0 allow for arbitrary code execution when loading a crafted .keras model archive, even when safe_mode=True. The issue arises because the archive’s config.json is parsed before layer deserialization. This can invoke keras.config.enable_unsafe_deserialization(), effectively disabling safe mode from within the loading process itself. An attacker can place this call first in the archive and then include a Lambda layer whose function is …

Duplicate Advisory This advisory has been withdrawn because it is a duplicate of GHSA-36rr-ww3j-vrjv. This link is maintained to preserve external references. Original Description The Keras Model.load_model method can be exploited to achieve arbitrary code execution, even with safe_mode=True. One can create a specially crafted .h5/.hdf5 model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed. This is achieved by crafting a special .h5 archive file …

It is possible to bypass the mitigation introduced in response to CVE-2025-1550, when an untrusted Keras v3 model is loaded, even when “safe_mode” is enabled, by crafting malicious arguments to built-in Keras modules. The vulnerability is exploitable on the default configuration and does not depend on user input (just requires an untrusted model to be loaded).

Duplicate Advisory This advisory has been withdrawn because it is a duplicate of GHSA-c9rc-mg46-23w3. This link is maintained to preserve external references. Original Description A safe mode bypass vulnerability in the Model.load_model method in Keras versions 3.0.0 through 3.10.0 allows an attacker to achieve arbitrary code execution by convincing a user to load a specially crafted .keras model archive.

Duplicate Advisory This advisory has been withdrawn because it is a duplicate of GHSA-48g7-3x6r-xfhp. This link is maintained to preserve external references. Original Description 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 …

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.

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.

A arbitrary code injection vulnerability in TensorFlow's Keras framework (<2.13) allows attackers to execute arbitrary code with the same permissions as the application using a model that allow arbitrary code irrespective of the application.