keras 3.12.4

Direct Vulnerabilities

Known vulnerabilities in the keras package. This does not include vulnerabilities belonging to this package’s dependencies.

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Vulnerability	Vulnerable Version
M Allocation of Resources Without Limits or Throttling keras is a Keras is a high-level neural networks API for Python.. Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling via the `safe_get_h5_dataset` function in `keras/src/saving/saving_lib.py`. An attacker can trigger an out-of-memory crash by supplying a malicious `.keras` or `.weights.h5` file to `keras.models.load_model()` or `load_weights()` that declares a huge HDF5 dataset shape while storing almost no data on disk. The loader materializes the dataset according to its declared shape without checking the on-disk storage size, so a crafted chunked, compressed, fill-value-only dataset can force unbounded memory allocation and terminate the process. This breaks machine-learning jobs that load untrusted models or weights, causing service disruption and failed model ingestion. How to fix Allocation of Resources Without Limits or Throttling? Upgrade `keras` to version 3.15.0 or higher.	[,3.15.0)
L Directory Traversal keras is a Keras is a high-level neural networks API for Python.. Affected versions of this package are vulnerable to Directory Traversal in the `filter_safe_tarinfos` process. An attacker can create symlink entries in a crafted tar archive that bypass directory validation by exploiting insufficient checks, potentially enabling file overwrite, file read, or directory escape by extracting the archive. How to fix Directory Traversal? There is no fixed version for `keras`.	[0,)

Vulnerability

Vulnerable Version

Allocation of Resources Without Limits or Throttling

keras is a Keras is a high-level neural networks API for Python..

Affected versions of this package are vulnerable to Allocation of Resources Without Limits or Throttling via the safe_get_h5_dataset function in keras/src/saving/saving_lib.py. An attacker can trigger an out-of-memory crash by supplying a malicious .keras or .weights.h5 file to keras.models.load_model() or load_weights() that declares a huge HDF5 dataset shape while storing almost no data on disk. The loader materializes the dataset according to its declared shape without checking the on-disk storage size, so a crafted chunked, compressed, fill-value-only dataset can force unbounded memory allocation and terminate the process. This breaks machine-learning jobs that load untrusted models or weights, causing service disruption and failed model ingestion.

How to fix Allocation of Resources Without Limits or Throttling?

Upgrade keras to version 3.15.0 or higher.

[,3.15.0)

Directory Traversal

keras is a Keras is a high-level neural networks API for Python..

Affected versions of this package are vulnerable to Directory Traversal in the filter_safe_tarinfos process. An attacker can create symlink entries in a crafted tar archive that bypass directory validation by exploiting insufficient checks, potentially enabling file overwrite, file read, or directory escape by extracting the archive.

How to fix Directory Traversal?

There is no fixed version for keras.

[0,)

keras@3.12.4

Multi-backend Keras

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Direct Vulnerabilities

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