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🐍 PyPI
Not in CISA KEV
HIGH severity

GHSA-36fq-jgmw-4r9c keras

HIGHFix: keras-team/keras#21429

GHSA-36fq-jgmw-4r9c is a high-severity (CVSS 7.3) Deserialization of Untrusted Data vulnerability in keras. A fix is available for keras — see the affected versions and patch details below.

Keras is vulnerable to Deserialization of Untrusted Data

Also known asCVE-2025-9906PYSEC-2025-76
Published
Sep 19, 2025
Updated
Sep 10, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Sep 19, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

  • A successful exploit gives an attacker total control of the affected component, not partial access.
  • 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-36fq-jgmw-4r9c.

EPSS Exploitation Probability

via FIRST.org ↗
0.2%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs10th percentile — riskier than 10% 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.

How urgent is this, really

GHSA-36fq-jgmw-4r9c plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.

Where this sits among everything scored

Of 377,166 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.

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

Arbitrary Code Execution in Keras

Keras versions prior to 3.11.0 allow for arbitrary code execution when loading a crafted .keras model archive, even when safe_mode=True.

The issue arises because the archive’s config.json is parsed before layer deserialization. This can invoke keras.config.enable_unsafe_deserialization(), effectively disabling safe mode from within the loading process itself. An attacker can place this call first in the archive and then include a Lambda layer whose function is deserialized from a pickle, leading to the execution of attacker-controlled Python code as soon as a victim loads the model file.

Exploitation requires a user to open an untrusted model; no additional privileges are needed. The fix in version 3.11.0 enforces safe-mode semantics before reading any user-controlled configuration and prevents the toggling of unsafe deserialization via the config file.

Affected versions: < 3.11.0 Patched version: 3.11.0

It is recommended to upgrade to version 3.11.0 or later and to avoid opening untrusted model files.

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIkerasall versions3.11.0pip install --upgrade 'keras==3.11.0'

Detection & mitigation playbook

Open-source dependency
  1. 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.

  2. Fix

    Update keras to 3.11.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-36fq-jgmw-4r9c is resolved across your whole dependency graph.

  3. 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.

  4. How O3 protects you

    O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-36fq-jgmw-4r9c can be triaged on real exposure rather than presence alone.

Tailored to GHSA-36fq-jgmw-4r9c. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

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 HatImportant
ProductFixed inAdvisory
Red Hat OpenShift AI 2.25rhoai/odh-modelmesh-runtime-adapter-rhel9:v2.25.1-1764825096RHSA-2025:23531

Frequently Asked Questions

### Arbitrary Code Execution in Keras Keras versions prior to 3.11.0 allow for arbitrary code execution when loading a crafted `.keras` model archive, even when `safe_mode=True`. The issue arises because the archive’s `config.json` is parsed before layer deserialization. This can invoke `keras.config.enable_unsafe_deserialization()`, effectively disabling safe mode from within the loading process itself. An attacker can place this call first in the archive and then include a `Lambda` layer whose function is deserialized from a pickle, leading to the execution of attacker-controlled Python co
O3 Security · Impact-Aware SCA

Is GHSA-36fq-jgmw-4r9c in your dependencies?

O3 Security finds GHSA-36fq-jgmw-4r9c across PyPI dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.

GHSA-36fq-jgmw-4r9c: keras RCE (High 7.3) | O3 Security