PYSEC-2026-3629
Vulnerability from pysec - Published: 2026-08-10 10:43 - Updated: 2026-08-10 11:01
VLAI
Details
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 3.12.3 and 3.15.0.
Severity
5.5 (Medium)
Impacted products
| Name | purl | keras | pkg:pypi/keras |
|---|
Aliases
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "keras",
"purl": "pkg:pypi/keras"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "3.12.3"
},
{
"introduced": "3.13.0"
},
{
"fixed": "3.15.0"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"0.2.0",
"0.3.0",
"0.3.1",
"0.3.2",
"0.3.3",
"1.0.0",
"1.0.1",
"1.0.2",
"1.0.3",
"1.0.4",
"1.0.5",
"1.0.6",
"1.0.7",
"1.0.8",
"1.1.0",
"1.1.1",
"1.1.2",
"1.2.0",
"1.2.1",
"1.2.2",
"2.0.0",
"2.0.1",
"2.0.2",
"2.0.3",
"2.0.4",
"2.0.5",
"2.0.6",
"2.0.7",
"2.0.8",
"2.0.9",
"2.1.0",
"2.1.1",
"2.1.2",
"2.1.3",
"2.1.4",
"2.1.5",
"2.1.6",
"2.10.0",
"2.10.0rc0",
"2.10.0rc1",
"2.11.0",
"2.11.0rc0",
"2.11.0rc1",
"2.11.0rc2",
"2.11.0rc3",
"2.12.0",
"2.12.0rc0",
"2.12.0rc1",
"2.13.1",
"2.13.1rc0",
"2.13.1rc1",
"2.14.0",
"2.14.0rc0",
"2.15.0",
"2.15.0rc0",
"2.15.0rc1",
"2.2.0",
"2.2.1",
"2.2.2",
"2.2.3",
"2.2.4",
"2.2.5",
"2.3.0",
"2.3.1",
"2.4.0",
"2.4.1",
"2.4.2",
"2.4.3",
"2.5.0rc0",
"2.6.0",
"2.6.0rc0",
"2.6.0rc1",
"2.6.0rc2",
"2.6.0rc3",
"2.7.0",
"2.7.0rc0",
"2.7.0rc2",
"2.8.0",
"2.8.0rc0",
"2.8.0rc1",
"2.9.0",
"2.9.0rc0",
"2.9.0rc1",
"2.9.0rc2",
"3.0.0",
"3.0.1",
"3.0.2",
"3.0.3",
"3.0.4",
"3.0.5",
"3.1.0",
"3.1.1",
"3.10.0",
"3.11.0",
"3.11.1",
"3.11.2",
"3.11.3",
"3.12.0",
"3.12.1",
"3.12.2",
"3.13.0",
"3.13.1",
"3.13.2",
"3.14.0",
"3.14.1",
"3.2.0",
"3.2.1",
"3.3.0",
"3.3.1",
"3.3.2",
"3.3.3",
"3.4.0",
"3.4.1",
"3.5.0",
"3.6.0",
"3.7.0",
"3.8.0",
"3.9.0",
"3.9.1",
"3.9.2"
]
}
],
"aliases": [
"CVE-2026-12480",
"GHSA-26c4-7vv6-867j"
],
"details": "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\u0027s 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 3.12.3 and 3.15.0.",
"id": "PYSEC-2026-3629",
"modified": "2026-08-10T11:01:44.117897Z",
"published": "2026-08-10T10:43:49.083911Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-12480"
},
{
"type": "WEB",
"url": "https://github.com/keras-team/keras/commit/8f987f11bf7512f0df4774a8f1557bba07dc2b49"
},
{
"type": "WEB",
"url": "https://github.com/keras-team/keras/commit/d5a88bdb137c0d3039b8f4bbbe8c7099925cc10c"
},
{
"type": "PACKAGE",
"url": "https://github.com/keras-team/keras"
},
{
"type": "WEB",
"url": "https://github.com/keras-team/keras/releases/tag/v3.12.3"
},
{
"type": "WEB",
"url": "https://github.com/keras-team/keras/releases/tag/v3.15.0"
},
{
"type": "WEB",
"url": "https://huntr.com/bounties/1875d257-5b03-4a69-ac70-e98653fa12c7"
},
{
"type": "PACKAGE",
"url": "https://pypi.org/project/keras"
},
{
"type": "ADVISORY",
"url": "https://github.com/advisories/GHSA-26c4-7vv6-867j"
}
],
"severity": [
{
"score": "CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:N",
"type": "CVSS_V3"
}
],
"summary": "Keras: HDF5 virtual datasets can disclose local files"
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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