Evidence used
- No CISA KEV confirmation is currently recorded.
- EPSS is 0.30% for the current model date.
BlackTreeCVE IntelligenceFull vulnerability report · 2026
CVE-2026-34753High confidence
vLLM affected by Server-Side Request Forgery (SSRF) in `download_bytes_from_url `
vllm-project · vllm
Official source article: GitHub GHSA-PF3H-QJGV-VCPR ↗. Check the applicable product and release in the original source.
5.4MediumCVSS 3.1
Scheduled
Patch availableMedium technical severity with no CISA KEV confirmation; remediate through the normal risk-based patch cycle unless local exposure raises the priority.
R
Open reassessment dashboard →Operational reassessment
Published severity in operational context
Published severityMedium→Operational priority:Medium, unchanged from published severity.unchanged
Compensating controls
- Validate the affected product branch and deploy the verified fixed release.
- Restrict the affected network interface to trusted sources where business-safe.
- Monitor vendor guidance and exploitation sources for a material change.
Verification
- Confirm that the asset runs vllm-project vllm and falls inside the recorded affected range.
- Verify the installed build against the product-specific fixed version after deployment.
- Validate exposure, authentication requirements and compensating controls in the actual environment.
- Reopen this reassessment when CVSS, KEV, EPSS, exploit evidence or remediation changes.
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 package | Affected range | First fixed version | Evidence |
|---|---|---|---|
| PyPIvllm | ECOSYSTEM: introduced 0.16.0; fixed 0.19.0 | 0.19.0 | OSV record ↗aggregator derived · 10 Sep 2026 |
| pipvllm | >= 0.16.0, < 0.19.0 | 0.19.0 | GitHub advisory ↗upstream repository advisory · 17 Jul 2026 |
Direct vendor intelligence
1 currentAuthoritative 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.
CVE-2026-34753 · CSAF 2.0 · revision 3 · finalRed Hat Product Securityvllm: vLLM: Server-Side Request Forgery allows access to internal services via controlled batch input
The vendor explicitly identifies these products as affected by this CVE.
- rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server
- rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server
- rhoai/odh-kserve-agent-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
- rhoai/odh-kserve-controller-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
- rhoai/odh-kserve-router-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
- rhoai/odh-kserve-storage-initializer-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
- rhoai/odh-vllm-cpu-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
- rhoai/odh-vllm-cuda-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-vllm-rocm-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
- Summary
- A flaw was found in vLLM. This server-side request forgery (SSRF) vulnerability allows an attacker who can control batch input JSON to force the vLLM batch runner to make arbitrary HTTP/HTTPS requests from the server. This can be exploited to access internal services, such as cloud metadata endpoints or internal HTTP APIs, potentially leading to information disclosure or further compromise of the host system.
- Remediation
- For more information visit https://access.redhat.com/errata/RHSA-2026:57380
Medium technical severity with no CISA KEV confirmation; remediate through the normal risk-based patch cycle unless local exposure raises the priority.
Patch available01
What, why and how
vLLM is an inference and serving engine for large language models (LLMs). From 0.16.0 to before 0.19.0, a server-side request forgery (SSRF) vulnerability in download_bytes_from_url allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions. This can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host. This vulnerability is fixed in 0.19.0.
What
vLLM is an inference and serving engine for large language models (LLMs). From 0.16.0 to before 0.19.0, a server-side request forgery (SSRF) vulnerability in download_bytes_from_url allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions. This can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host. This vulnerability is fixed in 0.19.0.
Why
The server follows an attacker-influenced destination, allowing requests to systems or services the attacker cannot reach directly.
How
An attacker operating through a network path may attempt exploitation with low privileges. If successful, the issue may cause the confidentiality, integrity or availability impact described by the vendor.
What
vLLM is an inference and serving engine for large language models (LLMs). From 0.16.0 to before 0.19.0, a server-side request forgery (SSRF) vulnerability in download_bytes_from_url allows any actor who can control batch input JSON to make the vLLM batch runner issue arbitrary HTTP/HTTPS requests from the server, without any URL validation or domain restrictions. This can be used to target internal services (e.g. cloud metadata endpoints or internal HTTP APIs) reachable from the vLLM host. This vulnerability is fixed in 0.19.0.
Why
The server follows an attacker-influenced destination, allowing requests to systems or services the attacker cannot reach directly.
How
An attacker operating through a network path may attempt exploitation with low privileges. 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 No confirmed evidence
?
Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.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 None recorded
?
Confirmed exploitation and public exploit material are separate signals. Attacks can occur without public proof-of-concept or exploit code.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
- a network path → Server-Side Request Forgery (SSRF) → cause the confidentiality, integrity or availability impact described by the vendor
- Attack surface
- Network
- Privileges required
- Low: a basic authenticated account is required
- User interaction
- None
- 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-918 ↗
CWE-918: Server-Side Request Forgery (SSRF). The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination.
- 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:L/PR:L/UI:N/S:U/C:L/I:N/A:LCommon 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.ACLowAttack complexity: No specialised conditions are required beyond attacker-controlled input.PRLowPrivileges required: The attacker needs basic user-level privileges.UINoneUser interaction: No action by another user is required.SUnchangedScope: The security impact remains within the vulnerable component's authority.CLowConfidentiality impact: A successful attack can cause a limited loss.INoneIntegrity impact: No direct loss is represented by this metric.ALowAvailability impact: A successful attack can cause a limited 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.
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
- rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server
- rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server
- rhoai/odh-kserve-agent-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
- rhoai/odh-kserve-controller-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
- rhoai/odh-kserve-router-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
- rhoai/odh-kserve-storage-initializer-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
- rhoai/odh-vllm-cpu-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
- rhoai/odh-vllm-cuda-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-vllm-rocm-rhel9 as a component of Red Hat OpenShift AI (RHOAI)
- Fixed
- registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64 as a component of Red Hat AI Inference Server 3.4
- registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:5da7a63ad71f6d047a35bfbd572a141be894bae84e60a2b22a237116c477a248_arm64 as a component of Red Hat AI Inference Server 3.4
- registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64 as a component of Red Hat AI Inference Server 3.4
- registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64 as a component of Red Hat AI Inference Server 3.4
- registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x as a component of Red Hat AI Inference Server 3.4
- registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64 as a component of Red Hat AI Inference Server 3.4
- registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le as a component of Red Hat AI Inference Server 3.4
- registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:a754d4d1126e98f203249414b3831182bb15ee6b3643452f167849defc642e4b_amd64 as a component of Red Hat Enterprise Linux AI 3.4
- registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:df5b6edd1bdb055219a7385b6d5923f3262c3a408f92da734e3027af55fd593f_amd64 as a component of Red Hat Enterprise Linux AI 3.4
- registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:8ea1233b1fb6b82d0aa69e9e4c47d2df242001280c11a61584066c27c5bec3a5_amd64 as a component of Red Hat Enterprise Linux AI 3.4
- registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:a127058368b396a8e8df17dc104469560f40d4451f9135320302568a63380035_arm64 as a component of Red Hat Enterprise Linux AI 3.4
- registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:b45f3bc25a7d3c9f3bfdc1de3be6db59f6c3109f62326d74fae14e832a797981_amd64 as a component of Red Hat Enterprise Linux AI 3.4
- registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:bac5d435d156c3ddcb581d3ff05fdce9f72bd617c5b8435cede16bb17b7bf12e_amd64 as a component of Red Hat Enterprise Linux AI 3.4
- registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:38ce057f97034bbb8eaea574d41cc7eebaeaa9265a4cdd966476dd3fac129c3c_amd64 as a component of Red Hat Enterprise Linux AI 3.4
- registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:5489bd1cdbb7f1a5ce80e9ad688b66c6bb8688b2b202a2a86b5c2205cdd6e126_amd64 as a component of Red Hat Enterprise Linux AI 3.4
- Action
- For more information visit https://access.redhat.com/errata/RHSA-2026:57380
- 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 6 Apr 2026 · Last source change 7 Apr 2026, 14:15 UTC · CWE-918 · Server-Side Request Forgery (SSRF)
CVE recordCVE.org · 5.2
CVSS sourceCNA
EPSS source FIRST · tracked since 2026-08-14
?
The date BlackTree first stored a score for this CVE from the daily FIRST EPSS feed.European sourceENISA EUVD · EUVD-2026-19349
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
View recent updates ↗What changed after publication
- Affected versionsThe structured affected or fixed version information changed.Red Hat Product Security ↗
- Before
- >= 0.16.0, < 0.19.0 · 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
- >= 0.16.0, < 0.19.0 · Fixed: For more information visit https://access.redhat.com/errata/RHSA-2026:57380
- Affected versionsThe structured affected or fixed version information changed.Red Hat Product Security ↗
- Before
- rhaiis/vllm-neuron-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-azure-rocm-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/bootc-rocm-rhel9 as a component of Red Hat Enterprise Linux AI (RHEL AI) 3; rhoai/odh-kserve-agent-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-kserve-controller-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-kserve-router-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-kserve-storage-initializer-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-vllm-cpu-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-vllm-cuda-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-vllm-rocm-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · Fixed: registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:5da7a63ad71f6d047a35bfbd572a141be894bae84e60a2b22a237116c477a248_arm64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le as a component of Red Hat AI Inference Server 3.4
- After
- rhaiis/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaiis/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; rhoai/odh-kserve-agent-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-kserve-controller-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-kserve-router-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-kserve-storage-initializer-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-vllm-cpu-rhel9 as a component of Red Hat OpenShift AI (RHOAI); rhoai/odh-vllm-cuda-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-vllm-rocm-rhel9 as a component of Red Hat OpenShift AI (RHOAI) · Fixed: registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:2e9fee8758cfe000f5b304c3de525fc812433fc2ad39122de86140f36c7be04f_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:5da7a63ad71f6d047a35bfbd572a141be894bae84e60a2b22a237116c477a248_arm64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:d2ed07d307845135c089bc7644b64734b9349d517abf746c9aa0aa23ed263da5_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:eb2ca896461f782d8c4c239d36545a1a749d17bebd9fdc0652024092614c69c2_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:3657354eb0edeb7e0e9aad2a434d7f264626b3d906f4e3e8502a95a73772ca41_s390x as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:54ad323a1a44f99991ca9d28cf0a364a44c5bbce143891936f6c668612c3ffb0_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b35ecb302c7f4e270f44bf70b7f7f8d053eb74761b1104fea346af9a8e089d09_ppc64le as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhelai3/bootc-aws-cuda-rhel9@sha256:a754d4d1126e98f203249414b3831182bb15ee6b3643452f167849defc642e4b_amd64 as a component of Red Hat Enterprise Linux AI 3.4; registry.redhat.io/rhelai3/bootc-azure-cuda-rhel9@sha256:df5b6edd1bdb055219a7385b6d5923f3262c3a408f92da734e3027af55fd593f_amd64 as a component of Red Hat Enterprise Linux AI 3.4; registry.redhat.io/rhelai3/bootc-azure-rocm-rhel9@sha256:8ea1233b1fb6b82d0aa69e9e4c47d2df242001280c11a61584066c27c5bec3a5_amd64 as a component of Red Hat Enterprise Linux AI 3.4; registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:a127058368b396a8e8df17dc104469560f40d4451f9135320302568a63380035_arm64 as a component of Red Hat Enterprise Linux AI 3.4; registry.redhat.io/rhelai3/bootc-cuda-rhel9@sha256:b45f3bc25a7d3c9f3bfdc1de3be6db59f6c3109f62326d74fae14e832a797981_amd64 as a component of Red Hat Enterprise Linux AI 3.4; registry.redhat.io/rhelai3/bootc-gcp-cuda-rhel9@sha256:bac5d435d156c3ddcb581d3ff05fdce9f72bd617c5b8435cede16bb17b7bf12e_amd64 as a component of Red Hat Enterprise Linux AI 3.4; registry.redhat.io/rhelai3/bootc-rocm-rhel9@sha256:38ce057f97034bbb8eaea574d41cc7eebaeaa9265a4cdd966476dd3fac129c3c_amd64 as a component of Red Hat Enterprise Linux AI 3.4; registry.redhat.io/rhelai3/disk-image-cuda-rhel9@sha256:5489bd1cdbb7f1a5ce80e9ad688b66c6bb8688b2b202a2a86b5c2205cdd6e126_amd64 as a component of Red Hat Enterprise Linux AI 3.4
CVE published
Fixed release or patch reference recorded
Fixed release recorded from official guidance
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.