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Full vulnerability report · 2026
CVE-2026-12484High confidence

Unsafe Deserialization in keras.layers.TorchModuleWrapper.from_config

keras-team · keras-team/keras

7.8HighCVSS 3.0
Recommended action
Within 7 days

High technical severity with public exploit material referenced by a structured source; prioritise exposed affected systems while verifying vendor guidance.

Mitigation available
Direct vendor intelligence

Authoritative vendor CSAF and VEX advisories

Structured product status and remediation from the issuing vendor. Product-state explanations are always visible; large lists can be searched or downloaded.

1 current
CVE-2026-12484 · CSAF 2.0 · revision 3 · finalRed Hat Product Securitykeras: torch: Keras: Arbitrary code execution via unsafe deserialization of PyTorch data
4 known affected

The vendor explicitly identifies these products as affected by this CVE.

  • rhoai/odh-pipeline-runtime-tensorflow-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
  • rhoai/odh-pipeline-runtime-tensorflow-rocm-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
  • rhoai/odh-workbench-jupyter-tensorflow-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
  • rhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
Summary
A flaw was found in Keras, specifically within the `keras.layers.TorchModuleWrapper.from_config` method. This vulnerability allows an attacker to perform unsafe deserialization of PyTorch pickle data. By processing untrusted Keras layer configurations, a local attacker could exploit this flaw, leading to arbitrary code execution. This occurs because the method does not enforce safe deserialization practices by default, potentially allowing malicious code to run on the system.
Remediation
To mitigate this issue, ensure that Red Hat OpenShift AI environments only process Keras layer configurations from trusted sources. Avoid loading or processing any untrusted Keras layer configurations, as this vulnerability can lead to arbitrary code execution.
Optional official sources

National CERT insights
?CERT means Computer Emergency Response Team; CSIRT is the closely related term Computer Security Incident Response Team.

Select the national-authority views to include. The exact source language is shown on each matched advisory. Your choice is remembered on this device and encoded in the shareable URL.

Official European source

ENISA European Vulnerability Database

Official EUVD identifiers, advisory evidence and known-exploited context. Missing fields are not treated as evidence of low risk.

1 current
ENISA EUVD identifier

EUVD-2026-45872

No EUVD known-exploited evidence

ENISA has published the identifier mapping but no EUVD description has been stored yet.

EUVD state
Present in the current official mapping
Known exploitation
Not present in the current ENISA EUVD known-exploited dataset. This is not proof of no exploitation.
ENISA score
Not supplied in the stored EUVD record
Advisory evidence
No linked advisory details stored yet
Recommended actionWithin 7 days

High technical severity with public exploit material referenced by a structured source; prioritise exposed affected systems while verifying vendor guidance.

Mitigation available
01

What, why and how

A vulnerability in keras-team/keras version 3.15.0 allows unsafe deserialization of attacker-controlled PyTorch pickle data through the public `keras.layers.TorchModuleWrapper.from_config` method. This method invokes `torch.load(..., weights_only=False)` without requiring an explicit unsafe opt-in, such as a `safe_mode=False` parameter. When called outside a `SafeModeScope(True)` context, the absence of an ambient safe mode state permits unsafe deserialization by default. This issue can lead to arbitrary code execution if untrusted Keras layer configurations are processed using this method. The vulnerability arises because the method does not enforce safe deserialization practices unless explicitly guarded by Keras safe mode.

What

A vulnerability in keras-team/keras version 3.15.0 allows unsafe deserialization of attacker-controlled PyTorch pickle data through the public `keras.layers.TorchModuleWrapper.from_config` method. This method invokes `torch.load(..., weights_only=False)` without requiring an explicit unsafe opt-in, such as a `safe_mode=False` parameter. When called outside a `SafeModeScope(True)` context, the absence of an ambient safe mode state permits unsafe deserialization by default. This issue can lead to arbitrary code execution if untrusted Keras layer configurations are processed using this method. The vulnerability arises because the method does not enforce safe deserialization practices unless explicitly guarded by Keras safe mode.

Why

Attacker-influenced serialised data is reconstructed as trusted objects, which can invoke dangerous application behaviour.

How

An attacker operating through local access may attempt exploitation without authentication after a user interaction. If successful, the issue may execute code or commands in the affected security context.

What

A vulnerability in keras-team/keras version 3.15.0 allows unsafe deserialization of attacker-controlled PyTorch pickle data through the public `keras.layers.TorchModuleWrapper.from_config` method. This method invokes `torch.load(..., weights_only=False)` without requiring an explicit unsafe opt-in, such as a `safe_mode=False` parameter. When called outside a `SafeModeScope(True)` context, the absence of an ambient safe mode state permits unsafe deserialization by default. This issue can lead to arbitrary code execution if untrusted Keras layer configurations are processed using this method. The vulnerability arises because the method does not enforce safe deserialization practices unless explicitly guarded by Keras safe mode.

Why

Attacker-influenced serialised data is reconstructed as trusted objects, which can invoke dangerous application behaviour.

How

An attacker operating through local access may attempt exploitation without authentication after a user interaction. If successful, the issue may execute code or commands in the affected security context.

02

Exploit reality and attack path

CVSS severity, EPSS forecast probability, public exploit material and CISA-confirmed exploitation are separate signals.

Observed exploitation
?Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.
No confirmed evidence

No CISA KEV match was present at the last successful refresh. This means no confirmation from that source, not proof of no exploitation.

Public PoC / exploit material
?Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.
Reference recorded

CISA Vulnrichment records proof-of-concept exploitation in its SSVC data. BlackTree has not independently executed or validated exploit material.

Likely attack path
local access → Deserialization of Untrusted Data → execute code or commands in the affected security context
Attack surface
Local
Privileges required
None: unauthenticated exploitation is possible
User interaction
Required interaction required
Attack complexity
Low: no specialised conditions are recorded
Security boundary
Unchanged: impact remains within the vulnerable component's security authority
Weakness
?CWE means Common Weakness Enumeration: a standard category for the underlying weakness.
CWE-502 ↗

CWE-502: Deserialization of Untrusted Data. The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.

CVSS vector
?CVSS means Common Vulnerability Scoring System. The vector records the metric values used to calculate technical severity.
CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H

Common Vulnerability Scoring System 3.0: the compact vector below is decoded into plain language.

AVLocalAttack vector: The attacker needs local access to the vulnerable system.ACLowAttack complexity: No specialised conditions are required beyond attacker-controlled input.PRNonePrivileges required: The attacker does not need an account or existing privileges.UIRequiredUser interaction: Another user must perform an action for exploitation to succeed.SUnchangedScope: The security impact remains within the vulnerable component's authority.CHighConfidentiality impact: A successful attack can cause a major loss.IHighIntegrity impact: A successful attack can cause a major loss.AHighAvailability impact: A successful attack can cause a major loss.
Post-exploitation / living off the land
After compromise, an attacker may use built-in shells, scripting engines, scheduled tasks and native network utilities for discovery, persistence or movement. This is a plausible LoTL path, not evidence that it has occurred for every attack.
UnauthenticatedRemote code executionCWE-502Public exploit reference
A

Official authority intelligence

Only matched European and national findings are included. Language selectors and unavailable sources are omitted.

03

Patch and workaround

Operational remediation based on structured source evidence.

Status
?Patch availability is based on structured fixed-version fields and authoritative update references. If no fix is verified, check the vendor advisory before making a change.
Mitigation available
Affected
rhoai/odh-pipeline-runtime-tensorflow-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-pipeline-runtime-tensorflow-rocm-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-tensorflow-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-tensorflow-rocm-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
Fixed
No fixed version is explicitly recorded in the structured CVE data.
Action
No verified patch reference is present in the current structured sources. Check the vendor advisory before making a change.
Workaround
To mitigate this issue, ensure that Red Hat OpenShift AI environments only process Keras layer configurations from trusted sources. Avoid loading or processing any untrusted Keras layer configurations, as this vulnerability can lead to arbitrary code execution.
04

Evidence and provenance

Published 19 Jul 2026 · Last source change 20 Jul 2026, 13:45 UTC · CWE-502 · Deserialization of Untrusted Data

CVE recordCVE.org · 5.2
CVSS sourceCNA
EPSS source
?The date BlackTree first stored a score for this CVE from the daily FIRST EPSS feed.
FIRST · tracked since 2026-08-14
European sourceENISA EUVD · EUVD-2026-45872
Product sourceVendor CSAF · Red Hat Product Security
Remediation sourceVendor CSAF · Red Hat Product Security
CWE sourceCNA
NVD statusNVD awaiting enrichment

Core structured fields are present and their contributing authorities are shown above.

Material change intelligence

What changed after publication

View recent updates →

No material field changes have been recorded since change tracking began. Routine source refreshes and cosmetic edits are intentionally excluded.

Material fields only · duplicate refreshes suppressed · history retained for the configured operational retention period
CVE published
ENISA EUVD · EUVD-2026-45872↗Vendor CSAF · Red Hat Product Security↗CISA Vulnrichment SSVC evidence↗CVE authority or vendor reference↗Official CVE record (CNA and ADP data)↗NVD record↗
Technical terms and abbreviations used in this report
CVE
Common Vulnerabilities and Exposures: the public identifier for one disclosed vulnerability.
CVSS
Common Vulnerability Scoring System: a technical severity framework; it is not patching priority by itself.
EPSS
Exploit Prediction Scoring System: FIRST's estimate of the probability that exploitation activity will be observed in the next 30 days; it is a forecast, not confirmation.
CWE
Common Weakness Enumeration: the standard category describing the underlying software or hardware weakness.
CNA
CVE Numbering Authority: an organisation authorised to assign and publish CVE records.
CISA ADP
Cybersecurity and Infrastructure Security Agency Authorized Data Publisher: structured enrichment added to a CVE record.
NVD
National Vulnerability Database: NIST's enrichment service for CVE records.
CERT / CSIRT
A computer security incident response team that publishes warnings or coordinates incident response.
PoC
Proof of concept: public material that demonstrates or helps reproduce exploitation.
CSAF
Common Security Advisory Framework: a machine-readable format for security advisories.
LoTL
Living off the land: abuse of legitimate tools or system functions during an attack.
Free version - for non-commercial use only.CVE-2026-12484 · cve.blacktree.nl