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Full vulnerability report · 2025
CVE-2025-14929High confidence

Hugging Face Transformers X-CLIP Checkpoint Conversion Deserialization of Untrusted Data Remote Code Execution Vulnerability

Hugging Face · Transformers

7.8HighCVSS 3.0
Recommended action
Within 7 days

High technical severity; prioritise exposed affected systems while verifying vendor guidance.

Patch 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-2025-14929 · CSAF 2.0 · revision 3 · finalRed Hat Product Securitytransformers: code execution when processing a malicious X-CLIP model file
30 known affected

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

  • openshift-lightspeed-tech-preview/lightspeed-rag-tool-rhel9 as a component of OpenShift Lightspeed
  • openshift-lightspeed/lightspeed-service-api-rhel9 as a component of OpenShift Lightspeed
  • rhaiis-preview/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server
  • rhaiis/model-opt-cuda-rhel9 as a component of Red Hat AI Inference Server
  • rhaiis/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server
  • rhaiis/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server
  • rhaiis/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server
  • rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server
  • ansible-automation-platform-25/lightspeed-chatbot-rhel8 as a component of Red Hat Ansible Automation Platform 2
  • ansible-automation-platform-26/lightspeed-chatbot-rhel9 as a component of Red Hat Ansible Automation Platform 2
  • rhelai1/bootc-amd-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI)
  • rhelai1/bootc-aws-nvidia-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI)
Summary
A flaw was found in the Hugging Face Transformers library. The parsing of checkpoints fails to validate user-supplied data, causing a deserialization of untrusted data. An attacker can exploit this issue by providing a malicious X-CLIP model, resulting in arbitrary code execution in the context of the current process processing the file.
Remediation
For Red Hat OpenShift AI 2.25.3 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update: https://docs.redhat.com/en/documentation/red_hat_openshift_ai/
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-2025-204830

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; prioritise exposed affected systems while verifying vendor guidance.

Patch available
01

What, why and how

Hugging Face Transformers X-CLIP Checkpoint Conversion Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face Transformers. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of checkpoints. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-28308.

What

Hugging Face Transformers X-CLIP Checkpoint Conversion Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face Transformers. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of checkpoints. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-28308.

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

Hugging Face Transformers X-CLIP Checkpoint Conversion Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face Transformers. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of checkpoints. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-28308.

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.
None recorded

No exploit-tagged reference or CISA SSVC proof-of-concept state is currently recorded. Research may still exist outside the structured feeds.

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-502
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.
Patch available
Affected
openshift-lightspeed-tech-preview/lightspeed-rag-tool-rhel9 as a component of OpenShift Lightspeed; openshift-lightspeed/lightspeed-service-api-rhel9 as a component of OpenShift Lightspeed; rhaiis-preview/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaiis/model-opt-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-rocm-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-spyre-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; ansible-automation-platform-25/lightspeed-chatbot-rhel8 as a component of Red Hat Ansible Automation Platform 2; ansible-automation-platform-26/lightspeed-chatbot-rhel9 as a component of Red Hat Ansible Automation Platform 2; rhelai1/bootc-amd-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI); rhelai1/bootc-aws-nvidia-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI); rhelai1/bootc-azure-amd-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI); rhelai1/bootc-azure-nvidia-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI); rhelai1/bootc-gcp-nvidia-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI); rhelai1/bootc-intel-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI); rhelai1/bootc-nvidia-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI); rhelai1/instructlab-amd-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI); rhelai1/instructlab-intel-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI); rhelai1/instructlab-nvidia-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI); rhelai3/bootc-aws-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-azure-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/bootc-gcp-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhelai3/disk-image-cuda-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhoai/odh-built-in-detector-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-caikit-nlp-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-guardrails-detector-huggingface-runtime-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-ta-lmes-job-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-vllm-cpu-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
Fixed
registry.redhat.io/rhoai/odh-caikit-tgis-serving-rhel9@sha256:75bbf9ecb71475e1c84dca0be31d833107fe3341d3323314959d68331ddae82e_arm64 as a component of Red Hat OpenShift AI 2.25; registry.redhat.io/rhoai/odh-caikit-tgis-serving-rhel9@sha256:f90e965a1638402b4dec6bb022cbb1d1e992c8fd71d0b687f8562a9abf478dc4_amd64 as a component of Red Hat OpenShift AI 2.25; registry.redhat.io/rhoai/odh-vllm-cuda-rhel9@sha256:34fc1c2caaa4290d65e41ee75d30024b066f46b48e62d9f26c6f3cb0d3a78bae_arm64 as a component of Red Hat OpenShift AI 2.25; registry.redhat.io/rhoai/odh-vllm-cuda-rhel9@sha256:d779464858681ad4e38b97e6465d9466a56f525c5d66164d577747ac8f4ab89e_amd64 as a component of Red Hat OpenShift AI 2.25; registry.redhat.io/rhoai/odh-vllm-rocm-rhel9@sha256:55cae4fc505d942c4e32179da894dddf18627fbb74262cd0e7e42c7cfa54508d_amd64 as a component of Red Hat OpenShift AI 2.25; registry.redhat.io/rhoai/odh-kserve-agent-rhel9@sha256:1f2995ee9de8b0dd7e45fc26dcef5f5a7a2e38b2e564ab3e6460c4af76e22e82_arm64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-agent-rhel9@sha256:599c13b4ecdaf8cfe00bef9270d5ef4286c4f5a238bacf801f901973b152c694_ppc64le as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-agent-rhel9@sha256:8a8b9aa606fadb92796dc1310c4604c669570da12735fe73f73b65386c439556_s390x as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-agent-rhel9@sha256:fe135e808533f7267267fe2f6bfcba0f28d2184991d82ccd1b85eab6b815c3c9_amd64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-controller-rhel9@sha256:67dee2abb1b3db82eeafba21914fdb5904ce09b95f2888dea485a5a7df4d58e6_amd64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-controller-rhel9@sha256:861d9c9ff292c8baf9f541a384ab323943c1d0aec29349dde7ac957d2dde7ee7_ppc64le as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-controller-rhel9@sha256:9c240f185478ac30dd82c9b55c0a882a45744c08a6f698da754872b0eeb9103e_arm64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-controller-rhel9@sha256:afb0fb167b488bb53706e2979bd58b4ee88f2624642b76dccc3a7e3f7ab0aa7e_s390x as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-router-rhel9@sha256:25306815d697653646bda1a84d7efc28403e1c62b3bb8a144319854ad527771d_arm64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-router-rhel9@sha256:4fb1f0d669a402cfeb3b7c241a1a095a245c004d553807eb1760bda41c697d9e_amd64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-router-rhel9@sha256:a0e6b5b5133598ee3aa52b2047889105269d90467dea0541885ce3ee07e90235_ppc64le as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-router-rhel9@sha256:b54a836af6a4837a3bbb95e2575ef5d33a380af9e18fcda29b005d654c1149a1_s390x as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9@sha256:926c814e6bd2362ace77e131690edec47d7cc4f5a7317a3f882cb1b9f38e330b_amd64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9@sha256:b342f7d470688180c3f91b9c8652aaee8250a036a0c8f550d31ea5816d2c8cab_s390x as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9@sha256:b67292b8828b41361925def921ba2713b4d7eaa83b2088e0ad21e44ba52eb228_ppc64le as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-kserve-storage-initializer-rhel9@sha256:bb3d89ff8b3e2f5072f5a645c8e3d79ee1d1a618f7bab6b39f7f1d12a33d44f0_arm64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-openvino-model-server-rhel9@sha256:8c7183a236dc4572833eb5dc99d6d08919a0eece1d1f346a5a7195c7cc30fcd7_amd64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-pipeline-runtime-pytorch-llmcompressor-cuda-py312-rhel9@sha256:12128f22697ec726d3cfa2b3eee1175976a87c4fab3aa6dcd89d9abe67093d0c_amd64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-training-cuda121-torch24-py311-rhel9@sha256:a916006227256e1bb7ca45961906aac51a87c480ca21a9446e549cf56d218215_amd64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-training-cuda124-torch25-py311-rhel9@sha256:5dad91aca0bca6b71596b2d7f9e62df7049c00c6a2e8f94fd3b6a20eb659a29c_amd64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-training-cuda128-torch28-py312-rhel9@sha256:5c118be16bd4ae3860ebae64acecaac1988d8a474d00a7951f603a6c58e24038_amd64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-training-operator-rhel9@sha256:dacae7408e98047615c97bf8864f2564fb05c9681193b22cbc4074d3095ab524_amd64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-training-operator-rhel9@sha256:e2565c013f3df99522c8c3bb0c5ab8a681f69bd7af21d939ad7274edd91cfeff_ppc64le as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-training-operator-rhel9@sha256:fb8041555c7636381e8d6bc412a247d57e01d9a2e908deef3e8749144be1b461_arm64 as a component of Red Hat OpenShift AI 3.3; registry.redhat.io/rhoai/odh-training-rocm62-torch24-py311-rhel9@sha256:1859e6721718dfd4bcb9015cee436853384b556f51339b439bc56adf42ffc2d3_amd64 as a component of Red Hat OpenShift AI 3.3; and 8 more
Action
For Red Hat OpenShift AI 2.25.3 see the following documentation, which will be updated shortly for this release, for important instructions on how to upgrade your cluster and fully apply this errata update: https://docs.redhat.com/en/documentation/red_hat_openshift_ai/
Workaround
No verified workaround is recorded. Limit untrusted access and use least privilege until authoritative guidance is available.
04

Evidence and provenance

Published 23 Dec 2025 · Last source change 24 Dec 2025, 16:24 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-2025-204830
Product sourceVendor CSAF · Red Hat Product Security
Remediation sourceVendor CSAF · Red Hat Product Security
CWE sourceCNA
NVD statusNVD enriched

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-2025-204830↗Vendor CSAF · Red Hat Product Security↗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-2025-14929 · cve.blacktree.nl