CVE-2026-1462
A vulnerability in the class of the package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of models, even when .
Published Apr 13, 2026 · corrected/updated Jul 15, 2026 · keras keras · evidence confidence high
A vulnerability in the class of the package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of models, even when . This bypasses the security guarantees of and enables arbitrary attacker-controlled code execution during model inference under the victim's privileges. The issue arises due to the unconditional loading of external SavedModels, serialization of attacker-controlled file paths, and the lack of validation in the method.
Grounded briefings
Catalog interpretations for three audiences. Each brief cites ingested metadata and the original advisories below. They are not independent research and they do not invent exploit steps.
Executive
CVE-2026-1462 is a high public vulnerability record for keras keras. It is not currently listed in the CISA Known Exploited catalog. Check the linked vendor or CERT advisories for patch status. This summary is a catalog interpretation of the ingested metadata and cites the original advisories below.
System administrator
Identify whether keras keras is in your estate, then compare installed versions with the affected ranges. Affected packages or ranges on record: keras/keras (3.13.0); redhat/openshift ai. Check the linked vendor or CERT advisories for patch status. Prefer the vendor or distribution advisory linked in Sources before changing production systems. This is a catalog interpretation, not a runbook.
Security analyst
Signals on file: CVSS 7.8, EPSS 0.4%. It is not currently listed in the CISA Known Exploited catalog. Weakness classes: CWE-502. Sources contributing to this record: cve_org, epss, nvd. Public exploit references, when present, are outbound links only and are not proof of active exploitation. This is a catalog interpretation of stored metadata.
Exploitation and remediation
Exploitation evidence
Not listed as known exploited in CISA KEV. Not in the CISA KEV catalog.
Patch and remediation
No required-action text is stored yet. Use the vendor or CERT links in Sources for patch availability.
Safe detection guidance
Compare installed versions with the affected ranges below and verify the advisory from the original publisher. This site stores metadata and outbound links only; it does not ship exploit code or proof-of-concept payloads.
Code
The project this record applies to. Exploit listings stay in References.
Timeline
Detection, updates, and resolution for this record.
Apr 13, 2026 · NIST NVD
Detected
A vulnerability in the class of the package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of models, even when . This bypasses the security guarantees of and enables arbitrary attacker-controlled code execution during model inference under the victim's privileges. The issue arises due to the unconditional loading of external SavedModels, ser
Jul 15, 2026 · NIST NVD
Updated
A vulnerability in the class of the package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of models, even when . This bypasses the security guarantees of and enables arbitrary attacker-controlled code execution during model inference under the victim's privileges. The issue arises due to the unconditional loading of external SavedModels, ser
Affected
- cpe keras/keras3.13.0
- cpe redhat/openshift aiversion unspecified
Sources
- CVE.orgVulnerability recordCVE-2026-1462
Official CVE identifier
cve.org - FIRST EPSSExploitation probabilityCVE-2026-1462
30-day exploitation probability 0.4%
first.org - NIST NVDVulnerability recordCVE-2026-1462
A vulnerability in the class of the package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of models, even when . This bypasses the security guarantees of and enables
nvd.nist.gov