GHSA-26C4-7VV6-867J
Vulnerability from github – Published: 2026-07-01 18:31 – Updated: 2026-08-07 20:28
VLAI
Summary
Keras: HDF5 virtual datasets can disclose local files
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)
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "keras"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "3.12.3"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "keras"
},
"ranges": [
{
"events": [
{
"introduced": "3.13.0"
},
{
"fixed": "3.15.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-12480"
],
"database_specific": {
"cwe_ids": [
"CWE-73"
],
"github_reviewed": true,
"github_reviewed_at": "2026-08-07T20:28:58Z",
"nvd_published_at": "2026-07-01T17:16:19Z",
"severity": "MODERATE"
},
"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": "GHSA-26c4-7vv6-867j",
"modified": "2026-08-07T20:28:58Z",
"published": "2026-07-01T18:31:51Z",
"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"
}
],
"schema_version": "1.4.0",
"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"
}
Loading…
Loading…
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.
Loading…
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.
Loading…
Loading…