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)?

If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

How does O3 Security protect against GHSA-qg93-c7p6-gg7f?

O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-qg93-c7p6-gg7f can be triaged on real exposure rather than presence alone.

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 14th 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 August 12, 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

GHSA-qg93-c7p6-gg7f — keras

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.

Vulnerability in Keras Model.load_model Leading to Arbitrary Local File Loading and SSRF

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

Published

Oct 29, 2025

Updated

Aug 12, 2026

Affected

1 pkg

Patched

1 / 1

Exploits

None indexed

Exploitation data as of Sep 25, 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 CVEs14th percentile — riskier than 14% of all scored CVEsHighest risk

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

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
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-qg93-c7p6-gg7f can be triaged on real exposure rather than presence alone.

Tailored to GHSA-qg93-c7p6-gg7f. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

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

Is GHSA-qg93-c7p6-gg7f in your dependencies?

O3 Security finds GHSA-qg93-c7p6-gg7f across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.

Scan my dependencies How O3 SCA works