High technical severity with public exploit material referenced by a structured source; 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-2026-45804 · CSAF 2.0 · revision 3 · finalRed Hat Product Securitydiffusers: Diffusers: Arbitrary code execution due to trust_remote_code guard bypass
8 known affected
The vendor explicitly identifies these products as affected by this CVE.
rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server
rhaii/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server
rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server
rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server
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
Summary
A flaw was found in Diffusers, a library for pretrained diffusion models. A remote attacker could exploit this vulnerability by crafting a malicious Hub repository with custom Python pipeline code. The `DiffusionPipeline.from_pretrained` flow can bypass the `trust_remote_code` security mechanism, allowing the execution of arbitrary code on the system when a user interacts with the malicious repository. This could lead to high impact on confidentiality, integrity, and availability of the affected system.
Remediation
For Red Hat OpenShift AI 3.4.4 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-2026-44711
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.
Patch available
01
What, why and how
Diffusers is the a library for pretrained diffusion models. Prior to 0.38.0, Diffusers' DiffusionPipeline.from_pretrained flow can bypass the trust_remote_code guard because download() validates model_index.json and custom pipeline code before later loading from a cached folder that can change, allowing a Hub repository with custom .py pipeline code to execute through the custom pipeline flow without passing custom_pipeline or trust_remote_code=True. This issue is fixed in version 0.38.0.
What
Diffusers is the a library for pretrained diffusion models. Prior to 0.38.0, Diffusers' DiffusionPipeline.from_pretrained flow can bypass the trust_remote_code guard because download() validates model_index.json and custom pipeline code before later loading from a cached folder that can change, allowing a Hub repository with custom .py pipeline code to execute through the custom pipeline flow without passing custom_pipeline or trust_remote_code=True. This issue is fixed in version 0.38.0.
Why
The product checks the state of a resource before using that resource, but the resource's state can change between the check and the use in a way that invalidates the results of the check.
How
An attacker operating through a network path may attempt exploitation without authentication after a user interaction. If successful, the issue may cause the confidentiality, integrity or availability impact described by the vendor.
What
Diffusers is the a library for pretrained diffusion models. Prior to 0.38.0, Diffusers' DiffusionPipeline.from_pretrained flow can bypass the trust_remote_code guard because download() validates model_index.json and custom pipeline code before later loading from a cached folder that can change, allowing a Hub repository with custom .py pipeline code to execute through the custom pipeline flow without passing custom_pipeline or trust_remote_code=True. This issue is fixed in version 0.38.0.
Why
The product checks the state of a resource before using that resource, but the resource's state can change between the check and the use in a way that invalidates the results of the check.
How
An attacker operating through a network path may attempt exploitation without authentication after a user interaction. If successful, the issue may cause the confidentiality, integrity or availability impact described by the vendor.
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
A structured CVE source labels at least one public reference as exploit material. BlackTree has not independently validated that it is safe, reliable or weaponised.
Likely attack path
a network path → Time-of-check Time-of-use (TOCTOU) Race Condition → cause the confidentiality, integrity or availability impact described by the vendor
Attack surface
Network
Privileges required
None: unauthenticated exploitation is possible
User interaction
Required interaction required
Attack complexity
High: exploitation depends on specific conditions
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-367: Time-of-check Time-of-use (TOCTOU) Race Condition. The product checks the state of a resource before using that resource, but the resource's state can change between the check and the use in a way that invalidates the results of the check.
CVSS vector ?CVSS means Common Vulnerability Scoring System. The vector records the metric values used to calculate technical severity.
CVSS:3.1/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H
Common Vulnerability Scoring System 3.1: the compact vector below is decoded into plain language.
AVNetworkAttack vector: The vulnerable component can be reached over a network.ACHighAttack complexity: Successful exploitation depends on specific conditions outside the attacker's direct control.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
No specific living-off-the-land technique is confirmed in the structured sources. Monitor normal administration tools for activity inconsistent with the affected service's baseline.
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
rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; 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
Fixed
registry.redhat.io/rhoai/odh-th06-cuda130-torch210-py312-rhel9@sha256:5e8cac66dc108c958173412601f1c37e777f53209ff9166add58a011823c87f6_arm64 as a component of Red Hat OpenShift AI 3.4; registry.redhat.io/rhoai/odh-th06-cuda130-torch210-py312-rhel9@sha256:ea69ab01a3aaeba9b4db8abbace9014a3044e0f0f13fb7aaaef93acf72f50248_amd64 as a component of Red Hat OpenShift AI 3.4; registry.redhat.io/rhoai/odh-th06-rocm64-torch291-py312-rhel9@sha256:b2a253f3dc063a8e1cee441640cce13fcf04918ce453be8f5d488301b1458a91_amd64 as a component of Red Hat OpenShift AI 3.4; registry.redhat.io/rhoai/odh-training-cuda128-torch29-py312-rhel9@sha256:d5cc3e7ad03f83104f3d1d4038fb2a4fa11dfa7501ce6be28ac568459a56f089_amd64 as a component of Red Hat OpenShift AI 3.4; registry.redhat.io/rhoai/odh-training-rocm64-torch29-py312-rhel9@sha256:bd98eb46f3f5e676457617ca0bfa7117610abf9d69b6082cb37b9a880af26eb2_amd64 as a component of Red Hat OpenShift AI 3.4
Action
For Red Hat OpenShift AI 3.4.4 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. If business-safe, reduce exposure to the affected interface and allow only trusted sources until authoritative guidance is available.
04
Evidence and provenance
Published 15 Jul 2026 · Last source change 15 Jul 2026, 17:27 UTC · CWE-367 · Time-of-check Time-of-use (TOCTOU) Race Condition
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-2026-44711
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.
Affected versionsThe structured affected or fixed version information changed.
Before
rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; 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; rhoai/odh-openvino-model-server-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · Fixed: registry.redhat.io/rhoai/odh-th06-cuda130-torch210-py312-rhel9@sha256:5e8cac66dc108c958173412601f1c37e777f53209ff9166add58a011823c87f6_arm64 as a component of Red Hat OpenShift AI 3.4; registry.redhat.io/rhoai/odh-th06-cuda130-torch210-py312-rhel9@sha256:ea69ab01a3aaeba9b4db8abbace9014a3044e0f0f13fb7aaaef93acf72f50248_amd64 as a component of Red Hat OpenShift AI 3.4; registry.redhat.io/rhoai/odh-th06-rocm64-torch291-py312-rhel9@sha256:b2a253f3dc063a8e1cee441640cce13fcf04918ce453be8f5d488301b1458a91_amd64 as a component of Red Hat OpenShift AI 3.4; registry.redhat.io/rhoai/odh-training-cuda128-torch29-py312-rhel9@sha256:d5cc3e7ad03f83104f3d1d4038fb2a4fa11dfa7501ce6be28ac568459a56f089_amd64 as a component of Red Hat OpenShift AI 3.4; registry.redhat.io/rhoai/odh-training-rocm64-torch29-py312-rhel9@sha256:bd98eb46f3f5e676457617ca0bfa7117610abf9d69b6082cb37b9a880af26eb2_amd64 as a component of Red Hat OpenShift AI 3.4
After
rhaii/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cpu-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; 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 · Fixed: registry.redhat.io/rhoai/odh-th06-cuda130-torch210-py312-rhel9@sha256:5e8cac66dc108c958173412601f1c37e777f53209ff9166add58a011823c87f6_arm64 as a component of Red Hat OpenShift AI 3.4; registry.redhat.io/rhoai/odh-th06-cuda130-torch210-py312-rhel9@sha256:ea69ab01a3aaeba9b4db8abbace9014a3044e0f0f13fb7aaaef93acf72f50248_amd64 as a component of Red Hat OpenShift AI 3.4; registry.redhat.io/rhoai/odh-th06-rocm64-torch291-py312-rhel9@sha256:b2a253f3dc063a8e1cee441640cce13fcf04918ce453be8f5d488301b1458a91_amd64 as a component of Red Hat OpenShift AI 3.4; registry.redhat.io/rhoai/odh-training-cuda128-torch29-py312-rhel9@sha256:d5cc3e7ad03f83104f3d1d4038fb2a4fa11dfa7501ce6be28ac568459a56f089_amd64 as a component of Red Hat OpenShift AI 3.4; registry.redhat.io/rhoai/odh-training-rocm64-torch29-py312-rhel9@sha256:bd98eb46f3f5e676457617ca0bfa7117610abf9d69b6082cb37b9a880af26eb2_amd64 as a component of Red Hat OpenShift AI 3.4
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.
