How severe is GHSA-qg93-c7p6-gg7f?

No CVSS score has been assigned to GHSA-qg93-c7p6-gg7f yet. Review the advisory details and affected package list to assess your exposure.

Which packages are affected by GHSA-qg93-c7p6-gg7f?

GHSA-qg93-c7p6-gg7f affects the following packages: keras (PyPI). Ecosystems affected: PyPI.

How do I fix GHSA-qg93-c7p6-gg7f?

Update keras to 3.12.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-qg93-c7p6-gg7f is resolved across your whole dependency graph.

How do I detect GHSA-qg93-c7p6-gg7f in my PyPI dependencies?

Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for keras, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

How do I mitigate GHSA-qg93-c7p6-gg7f if there is no patch (or I can't update yet)?

Do not deserialise data from untrusted sources: where the format allows it, restrict deserialisation to an explicit allowlist of expected types, and prefer a data-only format (JSON, Protobuf) over one that can reconstruct arbitrary objects until you can upgrade.

Is GHSA-qg93-c7p6-gg7f actively exploited in the wild?

No public exploit code has been indexed for GHSA-qg93-c7p6-gg7f yet. This does not mean the vulnerability cannot be exploited — absence of public exploits does not imply safety. Apply the recommended fix and use O3 Security to monitor your exposure.

What is the EPSS score for GHSA-qg93-c7p6-gg7f?

GHSA-qg93-c7p6-gg7f has an EPSS (Exploit Prediction Scoring System) score of 0.2%, placing it in the 16th percentile of all CVEs. EPSS is maintained by FIRST.org and estimates the probability that a vulnerability will be exploited in the wild within the next 30 days. This score indicates relatively lower exploitation probability, though the CVSS severity should still guide your patching priority.

What type of vulnerability is GHSA-qg93-c7p6-gg7f?

GHSA-qg93-c7p6-gg7f is classified as Deserialization of Untrusted Data (CWE-502). This weakness type describe the underlying flaw category, which helps determine the potential impact and the right class of mitigation. This is a high-impact weakness class that often enables remote code execution or data exposure.

When was GHSA-qg93-c7p6-gg7f published, and has it been updated?

GHSA-qg93-c7p6-gg7f was published on October 29, 2025 and was last updated on October 24, 2026. Advisory data evolves as severity scores, affected ranges, and exploit intelligence are revised — always check the latest version of the advisory before acting.

🐍 PyPI

Not in CISA KEV

Vulnerability in Keras Model.load_model Leading to Arbitrary Local File Loading and SSRFGHSA-qg93-c7p6-gg7f

Fix: keras-team/keras#21751

GHSA-qg93-c7p6-gg7f is a Deserialization of Untrusted Data vulnerability in keras. A fix is available for keras — see the affected versions and patch details below.

Also known asGHSA-mq84-hjqx-cwf2PYSEC-2026-1487

Published

Oct 29, 2025

Updated

Oct 24, 2026

Affected

1 pkg

Patched

1 / 1

Exploits

None indexed

Exploitation data as of Nov 10, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.

Exploitation and automatability from CISA’s SSVC triage for GHSA-qg93-c7p6-gg7f.

EPSS Exploitation Probability

via FIRST.org ↗

0.2%probability of exploitation in next 30 days

Lower Risk0.00%

Lower risk than most CVEs16th percentile — riskier than 16% of all scored CVEsHighest risk

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

Real-World Exposure

1 pkg affected

🐍keras

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

The Keras.Model.load_model method, including when executed with the intended security mitigation safe_mode=True, is vulnerable to arbitrary local file loading and Server-Side Request Forgery (SSRF).

This vulnerability stems from the way the StringLookup layer is handled during model loading from a specially crafted .keras archive. The constructor for the StringLookup layer accepts a vocabulary argument that can specify a local file path or a remote file path.

Arbitrary Local File Read: An attacker can create a malicious .keras file that embeds a local path in the StringLookup layer's configuration. When the model is loaded, Keras will attempt to read the content of the specified local file and incorporate it into the model state (e.g., retrievable via get_vocabulary()), allowing an attacker to read arbitrary local files on the hosting system.
Server-Side Request Forgery (SSRF): Keras utilizes tf.io.gfile for file operations. Since tf.io.gfile supports remote filesystem handlers (such as GCS and HDFS) and HTTP/HTTPS protocols, the same mechanism can be leveraged to fetch content from arbitrary network endpoints on the server's behalf, resulting in an SSRF condition.

The security issue is that the feature allowing external path loading was not properly restricted by the safe_mode=True flag, which was intended to prevent such unintended data access.

Affected Packages

1 total 1 fixed

Ecosystem	Package	Vulnerable range	Fix
🐍PyPI	`keras`	all versions	3.12.0`pip install --upgrade 'keras==3.12.0'`

Detection & mitigation playbook

Open-source dependency

Detect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for keras, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update keras to 3.12.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-qg93-c7p6-gg7f is resolved across your whole dependency graph.
Workarounds
Do not deserialise data from untrusted sources: where the format allows it, restrict deserialisation to an explicit allowlist of expected types, and prefer a data-only format (JSON, Protobuf) over one that can reconstruct arbitrary objects until you can upgrade.

Fixing This On Your OS

If you run this on a Linux distribution, patch through your package manager against the distro's own security advisory below — it tracks the exact backported fix for your release, which can ship on a different timeline (and sometimes a different severity) than the upstream project.

Red HatModerate

Workaround published by Red Hat

Mitigation for this issue is either not available or the currently available options do not meet the Red Hat Product Security criteria comprising ease of use and deployment, applicability to widespread installation base or stability.

Source: Red Hat security advisory for GHSA-qg93-c7p6-gg7f (CC BY 4.0)

Frequently Asked Questions

The Keras.Model.load_model method, including when executed with the intended security mitigation safe_mode=True, is vulnerable to arbitrary local file loading and Server-Side Request Forgery (SSRF). This vulnerability stems from the way the StringLookup layer is handled during model loading from a specially crafted .keras archive. The constructor for the StringLookup layer accepts a vocabulary argument that can specify a local file path or a remote file path. * Arbitrary Local File Read: An attacker can create a malicious .keras file that embeds a local path in the StringLookup layer's c

O3 Security · Impact-Aware SCA

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