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vulnerability / NVI-00015300 / Revision 2

Keras archive extraction utilities allow path traversal and arbitrary file writes

GitHub lists this reviewed advisory for keras. Published version ranges and source prerequisites are shown below; this is not an inspection of a visitor’s application.

source listedactiveGHSA-hqp4-2352-xf5rCVE-2026-11816

This report has not inspected your inventory, configuration, deployed build, reachability or evidence. A catalog listing and a severity score do not establish exposure or compromise.

01 / Meaning

Why it matters

Published affected context helps an owner decide what evidence to gather. This report has not examined your application.

CVSS

8.1 high

GitHub source severity; exploitability and local consequence require separate evidence.

As of · GitHub Advisory Database

02 / Applicability

The affected context

Affected versions, configuration prerequisites and known exclusions are public. Confirm each one against the deployed service.

keras

source listed
  • < 3.14.0

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.

GitHub Advisory Database

03 / Response

What to do next

  1. investigate

    Identify the exact component and deployed version, examine the linked publisher’s conditions, and retain evidence of any mitigation.

    Confirm current vendor support, compatibility and deployment evidence. This report has not tested a fix in your system. NIST National Vulnerability Database

A fixed release is not deployment evidence.

Check current vendor support and compatibility. After making a change, verify the deployed build and relevant runtime conditions. Keep the evidence, owner and time. An updated lockfile alone does not prove what is running.

04 / Limits

What this report cannot establish

  • Customer inventory, deployment, reachability and exploitation have not been inspected.
  • NVD lastModified does not establish freshness of every associated CPE record.
Read the collection and interpretation limits ↗

05 / Provenance

Sources you can inspect

Each assertion retains its source. Mirrored records may repeat one underlying publisher; additional links are not independent corroboration.

  1. GHSA-hqp4-2352-xf5r ↗

    GitHub Advisory Database · vendor advisory

    Observed . GitHub Advisory Database; CC-BY-4.0. Each advisory links its original attribution. Null normalizes fields.

  2. CVE-2026-11816 ↗

    NIST National Vulnerability Database · official catalog

    Observed . This product uses data from the NVD API but is not endorsed or certified by the NVD.

Public revision history

Only published revisions appear here. Routine fetches do not create a material-change event.

  1. Revision 2 ·

    Source facts changed; compare the preserved prior revision.

  2. Revision 1 ·

    First indexed by Null; source disclosure date is shown separately.

Read the public revision data ↗

The next step is private

Bring the question into your environment.

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