Product
19 published CVES affecting debian/keras, from public sources (CVE.org, NVD, CISA KEV, FIRST EPSS, OSV).
Newest publication first
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 s...
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, whi...
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...
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 regu...
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 Non...
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...
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 construc...
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...
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...
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...
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 crafte...
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. A...
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 c...
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 mo...
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 despit...
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 cra...
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 .keras model archive that, when loaded via Model.load_model, will trigger arbitrary code to be executed. This is achieved by crafti...
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.
Sources: CVE.org, NVD, CISA KEV, FIRST EPSS, OSV and public exploit-reference metadata. Product matching comes from the published CVE records. A proof-of-concept means public code exists; it is not evidence of exploitation.