Unbounded Frame Count in video/jpeg Base64 Data URL Processing Leads to OOM DoS in vllm-project/vllm
vllm-project · vllm-project/vllm
7.5HighCVSS 3.1
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.
Published severityHigh→Operational priority:Critical, raised one band.upgradedsince 22 Jul 2026
Evidence used
No CISA KEV confirmation is currently recorded.
A structured source references public exploit or proof-of-concept material.
The selected CVSS metric records a network-reachable, unauthenticated path with no user interaction.
EPSS is 0.90% 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
Confirm that the asset runs vllm-project 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 ranges1 source-attributed range
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.
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-2026-5497 · CSAF 2.0 · revision 3 · finalRed Hat Product Securityvllm: vLLM: Denial of Service via unbounded video frame processing
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
rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server
rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server
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-gaudi-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
Summary
A flaw was found in vLLM. An attacker can exploit this vulnerability by sending a specially crafted API request containing an excessive number of base64-encoded JPEG frames within a data URL. This unbounded processing of frames in the `VideoMediaIO.load_base64()` method leads to an Out-of-Memory (OOM) condition, causing the server to crash and resulting in a Denial of Service (DoS).
Remediation
For more information visit https://access.redhat.com/errata/RHSA-2026:69466
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-2026-36217
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
vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.
What
vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.
Why
The product does not properly control the allocation and maintenance of a limited resource.
How
An attacker operating through a network path may attempt exploitation without authentication or user interaction. If successful, the issue may disrupt the affected service.
What
vLLM versions 0.8.0 and later are vulnerable to an Out-of-Memory (OOM) Denial of Service (DoS) attack due to unbounded frame count processing in the `VideoMediaIO.load_base64()` method. When processing `video/jpeg` data URLs, the method splits the base64 data string on commas to extract individual JPEG frames without enforcing a frame count limit. An attacker can exploit this by crafting a single API request containing thousands of comma-separated base64-encoded JPEG frames in a data URL, causing the server to decode all frames into memory and crash due to excessive memory consumption. This vulnerability is reachable via the OpenAI-compatible chat completions API and does not require authentication.
Why
The product does not properly control the allocation and maintenance of a limited resource.
How
An attacker operating through a network path may attempt exploitation without authentication or user interaction. If successful, the issue may disrupt the affected service.
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 → Uncontrolled Resource Consumption → disrupt the affected service
Attack surface
Network
Privileges required
None: unauthenticated exploitation is possible
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-400: Uncontrolled Resource Consumption. The product does not properly control the allocation and maintenance of a limited resource.
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:N/UI:N/S:U/C:N/I:N/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.ACLowAttack complexity: No specialised conditions are required beyond attacker-controlled input.PRNonePrivileges required: The attacker does not need an account or existing privileges.UINoneUser interaction: No action by another user is required.SUnchangedScope: The security impact remains within the vulnerable component's authority.CNoneConfidentiality impact: No direct loss is represented by this metric.INoneIntegrity impact: No direct loss is represented by this metric.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.
NetworkUnauthenticatedDenial of serviceCWE-400Public exploit reference
A
Official authority intelligence
Only matched European and national findings are included. Language selectors and unavailable sources are omitted.
Ein Angreifer kann mehrere Schwachstellen in vllm ausnutzen, um Sicherheitsmaßnahmen zu umgehen, einen Denial-of-Service-Zustand zu verursachen, Daten zu manipulieren oder vertrauliche Informationen offenzulegen.
Official advisory ↗JVN iPedia · Japanese · JVNDB-2026-019815vLLMにおけるリソースの枯渇に関する脆弱性
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.
For more information visit https://access.redhat.com/errata/RHSA-2026:69466
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 11 Jun 2026 · Last source change 22 Jul 2026, 12:08 UTC · CWE-400 · Uncontrolled Resource Consumption
CVE recordCVE.org · 5.2
CVSS sourceOther authority
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-2026-36217
Product sourceVendor CSAF · Red Hat Product Security
Remediation sourceVendor CSAF · Red Hat Product Security
CWE sourceCNA
NVD statusNVD modified after enrichment
Core structured fields are present and their contributing authorities are shown above.
Affected versionsThe structured affected or fixed version information changed.
Before
rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; 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-gaudi-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-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@sha256:5da2be9bd0fa0c7344e8a0b84c0cb8f3a78a1c7fc5fc76db6a7daaa2d71324f9_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:94721a151f43ffdfe11b5551ac37cbcf5ff6b991345ca2fc300684cf0a740435_arm64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:97f7af7da4505d0420c49719bed4b49855abb1fa3193d65e5f201d0d501eeee7_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:4d9c1873e151f8c40f13a6b4c71b5f5aea5aca6e75d76a46ccb0784d48932bd7_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:6b855616de644893c5ee76cd6e496478762f941855f05e47bdac27aa259a7aae_ppc64le as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:8f3b2de1d5a8fdba70e937ff1ab95c0584cec32a4d58b1e636ea130a6461cea1_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b600c4226c78042f1a7d596f8f567dd2d9a97b5b3bb9b008d994c6bfb12bd7f1_s390x as a component of Red Hat AI Inference Server 3.4
After
rhaii/vllm-gaudi-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-neuron-rhel9 as a component of Red Hat AI Inference Server; rhaii/vllm-tpu-rhel9 as a component of Red Hat AI Inference Server; 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-gaudi-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-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@sha256:5da2be9bd0fa0c7344e8a0b84c0cb8f3a78a1c7fc5fc76db6a7daaa2d71324f9_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-cpu-rhel9@sha256:81c5994dc20321f8bd3969099eade78ef127cbd79f2d0ccea101c773e4c7fa4d_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:0d22e2301ce26ef68effda5d3a4a20381ad0ea34d1240e159e245bd44c2e125b_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:94721a151f43ffdfe11b5551ac37cbcf5ff6b991345ca2fc300684cf0a740435_arm64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:97f7af7da4505d0420c49719bed4b49855abb1fa3193d65e5f201d0d501eeee7_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-cuda-rhel9@sha256:e491c3b169ec77c37dbe0eb9c2b35dd976ca6e968f43b5d72ad5c4095f48d2ed_arm64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:1a30359d6ca5d0891e403b9b0a23c0ae1ad6e30a2429b9b42e246b874fe8859a_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-rocm-rhel9@sha256:4d9c1873e151f8c40f13a6b4c71b5f5aea5aca6e75d76a46ccb0784d48932bd7_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:11ad6e13c41728944d933c7abe01d1d10ecb3f8e8b4f0da50ec53b7df6f036a8_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:340cecbac6b168cb0414f242832f153e6f3655052bfd98252f9e2d507509cabf_ppc64le as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:40c5ed966b693ab9b6f1e71455c04eb42c5c0010c356ab5526218ab3034bede2_s390x as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:6b855616de644893c5ee76cd6e496478762f941855f05e47bdac27aa259a7aae_ppc64le as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:8f3b2de1d5a8fdba70e937ff1ab95c0584cec32a4d58b1e636ea130a6461cea1_amd64 as a component of Red Hat AI Inference Server 3.4; registry.redhat.io/rhaii/vllm-spyre-rhel9@sha256:b600c4226c78042f1a7d596f8f567dd2d9a97b5b3bb9b008d994c6bfb12bd7f1_s390x as a component of Red Hat AI Inference Server 3.4
Affected versionsThe structured affected or fixed version information changed.
Before
unspecified < 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
unspecified < 0.19.0 · Fixed: For more information visit https://access.redhat.com/errata/RHSA-2026:69466
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.