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Search Results (4 CVEs found)
| CVE | Vendors | Products | Updated | CVSS v3.1 |
|---|---|---|---|---|
| CVE-2026-12480 | 1 Keras-team | 1 Keras | 2026-07-06 | 5.5 Medium |
| 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. When the victim loads the model using `keras.models.load_model()` or `keras.saving.load_model()`, the external file is transparently read, leading to potential information disclosure. Fixed in versions 3.12.2 and 3.14.1. | ||||
| CVE-2026-12481 | 1 Keras-team | 1 Keras | 2026-07-06 | 8.8 High |
| 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 be deserialized. Affected call sites include `keras.layers.deserialize(config)`, `keras.models.clone_model(model)`, and any direct invocation of `Lambda.from_config(config)` without an enclosing `SafeModeScope(True)`. This vulnerability can be exploited to achieve arbitrary OS-level code execution in the context of the server or user process. | ||||
| CVE-2026-12479 | 1 Keras-team | 1 Keras | 2026-06-23 | 6.1 Medium |
| 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 model that, when saved or loaded, can escape the intended temporary working directory and perform unauthorized file system operations, such as creating directories or writing files in arbitrary locations. | ||||
| CVE-2026-11816 | 1 Keras-team | 1 Keras | 2026-06-12 | 8.1 High |
| 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. | ||||
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