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Full vulnerability report · 2026
CVE-2026-57516High confidence

Ray < 2.56.0 Unsafe Deserialization RCE via WebDataset Reader

Anyscale, Inc · Ray

Official source article: GitHub GHSA-HHRP-GW25-JR43 ↗. Check the applicable product and release in the original source.

8.6HighCVSS 4.0
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.

Patch available
R
Operational reassessment

Published severity in operational context

Open reassessment dashboard →
Published severityHighOperational priority:Critical, raised one band.upgradedsince 24 Sep 2026

Evidence used

  • No CISA KEV confirmation is currently recorded.
  • A structured source references public exploit or proof-of-concept material.
  • EPSS is 0.86% for the current model date.

Compensating controls

  • Validate the affected product branch and deploy the verified fixed release.
  • Restrict the affected network interface to trusted sources where business-safe.
  • Increase monitoring for the attack path and post-exploitation behaviour described in the report.

Verification

  1. Confirm that the asset runs Anyscale, Inc Ray and falls inside the recorded affected range.
  2. Verify the installed build against the product-specific fixed version after deployment.
  3. Validate exposure, authentication requirements and compensating controls in the actual environment.
  4. Reopen this reassessment when CVSS, KEV, EPSS, exploit evidence or remediation changes.
Mitigation target: Within 3 daysRemediation target: Within 90 days

This automated reassessment organises public evidence. It does not know asset exposure, business impact or control effectiveness and does not replace CVSS or a human risk decision.

Open-source package ranges2 source-attributed ranges

These OSV and GitHub advisory ranges apply only to the named package and ecosystem. A listed fixed version is not a universal product patch or proof that an update is installed.

Ecosystem and packageAffected rangeFirst fixed versionEvidence
PyPIrayECOSYSTEM: introduced 0; fixed 2.56.02.56.0OSV record ↗aggregator derived · 10 Sep 2026
pipray< 2.56.02.56.0GitHub advisory ↗upstream repository advisory · 24 Jul 2026
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-2026-57516 · CSAF 2.0 · revision 3 · finalRed Hat Product Securityray: Ray: Remote code execution via unsafe deserialization in WebDataset reader
15 known affected

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

  • rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server
  • rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3
  • rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
  • rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
  • rhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
  • 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-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
  • rhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
  • rhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
  • rhoai/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
  • rhoai/odh-workbench-jupyter-pytorch-rocm-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
Summary
A flaw was found in Ray. This unsafe deserialization vulnerability in the WebDataset reader allows a remote attacker to achieve arbitrary code execution. By supplying a specially crafted malicious tar archive to the read_webdataset() function, an attacker can trigger the unconditional deserialization of .pkl/.pickle or .pt/.pth entries, leading to the execution of arbitrary code within Ray remote workers.
Remediation
For more information visit https://access.redhat.com/errata/RHSA-2026:61627
Optional official sources

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

Choose official national sources for this report. Each advisory shows its original language. Your selection is remembered on this device and included in shared links.

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-2026-41089

No EUVD known-exploited evidence

Ray prior to 2.56.0 contains an unsafe deserialization vulnerability in the WebDataset reader that allows attackers to achieve remote code execution by supplying a malicious tar archive to the read_webdataset() function. The _default_decoder() function in webdataset_datasource.py unconditionally calls pickle.loads() on tar entries with .pkl/.pickle extensions and torch.load() with weights_only=False on .pt/.pth entries, executing arbitrary code inside Ray remote workers on every worker that processes the malicious archive.

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
8.6 · CVSS 4.0
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

Ray prior to 2.56.0 contains an unsafe deserialization vulnerability in the WebDataset reader that allows attackers to achieve remote code execution by supplying a malicious tar archive to the read_webdataset() function. The _default_decoder() function in webdataset_datasource.py unconditionally calls pickle.loads() on tar entries with .pkl/.pickle extensions and torch.load() with weights_only=False on .pt/.pth entries, executing arbitrary code inside Ray remote workers on every worker that processes the malicious archive.

What

Ray prior to 2.56.0 contains an unsafe deserialization vulnerability in the WebDataset reader that allows attackers to achieve remote code execution by supplying a malicious tar archive to the read_webdataset() function. The _default_decoder() function in webdataset_datasource.py unconditionally calls pickle.loads() on tar entries with .pkl/.pickle extensions and torch.load() with weights_only=False on .pt/.pth entries, executing arbitrary code inside Ray remote workers on every worker that processes the malicious archive.

Why

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

How

An attacker operating through a network path may attempt exploitation when the stated preconditions are met. If successful, the issue may execute code or commands in the affected security context.

What

Ray prior to 2.56.0 contains an unsafe deserialization vulnerability in the WebDataset reader that allows attackers to achieve remote code execution by supplying a malicious tar archive to the read_webdataset() function. The _default_decoder() function in webdataset_datasource.py unconditionally calls pickle.loads() on tar entries with .pkl/.pickle extensions and torch.load() with weights_only=False on .pt/.pth entries, executing arbitrary code inside Ray remote workers on every worker that processes the malicious archive.

Why

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

How

An attacker operating through a network path may attempt exploitation when the stated preconditions are met. 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

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 → Deserialization of Untrusted Data → execute code or commands in the affected security context
Attack surface
Network
Privileges required
None: unauthenticated exploitation is possible
User interaction
Active interaction required
Attack complexity
Low: no specialised conditions are recorded
Security boundary
Not a CVSS 4.0 base metric
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:4.0/AV:N/AC:L/AT:N/PR:N/UI:A/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Common Vulnerability Scoring System 4.0: the compact vector below is decoded into plain language.

AVNetworkAttack vector: The vulnerable component can be reached over a network.ACLowAttack complexity: No specialised conditions are required beyond attacker-controlled input.ATNoneAttack requirements: No additional deployment or execution condition is required.PRNonePrivileges required: The attacker does not need an account or existing privileges.UIActiveUser interaction: A user must take a deliberate action for exploitation to succeed.VCHighVulnerable-system confidentiality: A successful attack can cause a major loss.VIHighVulnerable-system integrity: A successful attack can cause a major loss.VAHighVulnerable-system availability: A successful attack can cause a major loss.SCNoneSubsequent-system confidentiality: No direct loss is represented by this metric.SINoneSubsequent-system integrity: No direct loss is represented by this metric.SANoneSubsequent-system availability: No direct loss is represented by this metric.
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.
NetworkUnauthenticatedRemote code executionCWE-502Public exploit reference
A

Official authority intelligence

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

ENISA EUVD · EUVD-2026-41089Official EUVD mapping

Ray prior to 2.56.0 contains an unsafe deserialization vulnerability in the WebDataset reader that allows attackers to achieve remote code execution by supplying a malicious tar archive to the read_webdataset() function. The _default_decoder() function in webdataset_datasource.py unconditionally calls pickle.loads() on tar entries with .pkl/.pickle extensions and torch.load() with weights_only=False on .pt/.pth entries, executing arbitrary code inside Ray remote workers on every worker that processes the malicious archive.

Official EUVD record ↗
Cyber Security Agency of Singapore · English · CSA-SB-20260708Security Bulletin 8 July 2026 [PDF, 1MB]]

The Cyber Security Agency of Singapore included this CVE in its official Security Bulletin 8 July 2026 [PDF, 1MB]], published on 8 July 2026. Open the linked bulletin for the product, severity and reference information published in that issue.

Official advisory ↗
JVN iPedia · Japanese · JVNDB-2026-022570anyscaleのrayにおける信頼できないデータのデシリアライゼーションに関する脆弱性

Ray 2.56.0未満のバージョンには、WebDatasetリーダーに安全でないデシリアライズの脆弱性が存在します。この脆弱性により、攻撃者は悪意のあるtarアーカイブをread_webdataset()関数に渡すことでリモートコード実行を達成できます。webdataset_datasource.py内の_default_decoder()関数は、.pkl/.pickle拡張子のtarエントリに対して無条件にpickle.loads()を呼び出し、.pt/.pthエントリに対してはweights_only=Falseの状態でtorch.load()を呼び出します。これにより、悪意のあるアーカイブを処理する各Rayリモートワーカー内で任意のコードが実行されます。

Official advisory ↗
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
Fixed
Action
For more information visit https://access.redhat.com/errata/RHSA-2026:61627
Workaround
A mitigation or workaround reference is available from the source linked below; validate it against the affected product and version.
04

Evidence and provenance

Published 1 Jul 2026 · Last source change 24 Sept 2026, 14:17 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-2026-41089
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 ↗
  1. Vendor guidanceAuthoritative vendor guidance changed: added remediation: github.com/GHSA-hhrp-gw25-jr43; removed remediation: access.redhat.com/CVE-2026-57516.
    Before
    patch: github.com/63469 · patch: github.com/63470 · patch: github.com/GHSA-hhrp-gw25-jr43 · 1 more references
    After
    patch: github.com/63469 · patch: github.com/63470 · patch: github.com/GHSA-hhrp-gw25-jr43 · 1 more references
    github.com ↗
  2. Affected versionsThe structured affected or fixed version information changed.
    Before
    < 2.56.0 · Fixed: For more information visit https://access.redhat.com/errata/RHSA-2026:61627
    After
    Ray: < 2.56.0 · Fixed: An authoritative update reference is available, but the fixed version is not recorded in the structured CVE fields. Check the linked vendor advisory for the applicable release.
    CNA ↗
  3. Affected versionsThe structured affected or fixed version information changed.
    Before
    Ray: < 2.56.0 · Fixed: An authoritative update reference is available, but the fixed version is not recorded in the structured CVE fields. Check the linked vendor advisory for the applicable release.
    After
    Ray: < 2.56.0 · Fixed: For more information visit https://access.redhat.com/errata/RHSA-2026:61627
    Red Hat Product Security ↗
  4. Affected versionsThe structured affected or fixed version information changed.
    Before
    rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server; rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); 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-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-pytorch-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); rhoai/odh-workbench-jupyter-trustyai-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · Fixed: registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64 as a component of Red Hat AI Inference Server 3.2; registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64 as a component of Red Hat AI Inference Server 3.2
    After
    rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server; rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); 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-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-pytorch-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); rhoai/odh-workbench-jupyter-trustyai-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · Fixed: registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64 as a component of Red Hat AI Inference Server 3.2; registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64 as a component of Red Hat AI Inference Server 3.2; registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:73bbc4560132c4aaa9bbd608ce9560eeae7b30bcc8f014da460afa5622139372_amd64 as a component of Red Hat AI Inference Server 3.3; registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:dc9887746020d299ecaac5d03221a8876b7024eeea79d82e1fb38afd955fb5c0_arm64 as a component of Red Hat AI Inference Server 3.3
    Red Hat Product Security ↗
  5. Affected versionsThe structured affected or fixed version information changed.
    Before
    < 2.56.0 · Fixed: An authoritative update reference is available, but the fixed version is not recorded in the structured CVE fields. Check the linked vendor advisory for the applicable release.
    After
    < 2.56.0 · Fixed: For more information visit https://access.redhat.com/errata/RHSA-2026:61627
    Red Hat Product Security ↗
  6. Vendor guidanceAuthoritative vendor guidance changed from remediation: access.redhat.com/CVE-2026-57516 to remediation: access.redhat.com/CVE-2026-57516.
    Before
    remediation: access.redhat.com/CVE-2026-57516
    After
    remediation: access.redhat.com/CVE-2026-57516
    Red Hat Product Security ↗
  7. Remediation statusRemediation status changed from Mitigation available to Patch available.
    Before
    Mitigation available
    After
    Patch available
    Red Hat Product Security ↗
  8. Affected versionsThe structured affected or fixed version information changed.
    Before
    rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-cuda-rhel9 as a component of Red Hat AI Inference Server; rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); 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-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-pytorch-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); rhoai/odh-workbench-jupyter-trustyai-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
    After
    rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server; rhelai3/bootc-gaudi-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhoai/odh-pipeline-runtime-datascience-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-pipeline-runtime-pytorch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-pipeline-runtime-pytorch-rocm-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); 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-vllm-gaudi-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-datascience-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-pytorch-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-pytorch-llmcompressor-cuda-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-workbench-jupyter-pytorch-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); rhoai/odh-workbench-jupyter-trustyai-cpu-py312-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · Fixed: registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:44224455142130c9594b4361fd21494c88244acb59b242f31ecb66019a5238dd_arm64 as a component of Red Hat AI Inference Server 3.2; registry.redhat.io/rhaiis/vllm-cuda-rhel9@sha256:e2a077acf23766ef900942296ce963a624d2c4b45ff22ca77cadeb94c051fd95_amd64 as a component of Red Hat AI Inference Server 3.2
    Red Hat Product Security ↗
Material fields only · duplicate refreshes suppressed · history retained for the configured operational retention period
CVE published
Fixed release or patch reference recorded
Fixed release recorded from official guidance
Fixed release recorded from official guidance
ENISA EUVD · EUVD-2026-41089↗Red Hat Product Security security advisory↗CISA Vulnrichment SSVC evidence↗Public exploit reference · GitHub GHSA-HHRP-GW25-JR43↗Patch or update reference↗Patch or update reference↗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-2026-57516 · cve.blacktree.nl