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WID-SEC-W-2026-0987
Vulnerability from csaf_certbund - Published: 2026-04-07 22:00 - Updated: 2026-04-07 22:00Summary
vllm: Mehrere Schwachstellen
Severity
Mittel
Notes
Das BSI ist als Anbieter für die eigenen, zur Nutzung bereitgestellten Inhalte nach den allgemeinen Gesetzen verantwortlich. Nutzerinnen und Nutzer sind jedoch dafür verantwortlich, die Verwendung und/oder die Umsetzung der mit den Inhalten bereitgestellten Informationen sorgfältig im Einzelfall zu prüfen.
Produktbeschreibung: Open Source vLLM ist eine Open-Source-Bibliothek für schnelle und effiziente Inferenz von Large Language Models (LLMs).
Angriff: Ein Angreifer kann mehrere Schwachstellen in vllm ausnutzen, um Dateien zu manipulieren, Sicherheitsmaßnahmen zu umgehen, vertrauliche Informationen offenzulegen oder einen Denial-of-Service-Zustand herbeizuführen.
Betroffene Betriebssysteme: - Sonstiges
- UNIX
Affected products
Known affected
1 product
| Product | Identifier | Version | Remediation |
|---|---|---|---|
|
Open Source vllm <0.19.0
Open Source / vllm
|
<0.19.0 |
Affected products
Known affected
1 product, the same list as for
CVE-2026-34753
Affected products
Known affected
1 product, the same list as for
CVE-2026-34753
Affected products
Known affected
1 product, the same list as for
CVE-2026-34753
References
7 references
{
"document": {
"aggregate_severity": {
"text": "mittel"
},
"category": "csaf_base",
"csaf_version": "2.0",
"distribution": {
"tlp": {
"label": "WHITE",
"url": "https://www.first.org/tlp/"
}
},
"lang": "de-DE",
"notes": [
{
"category": "legal_disclaimer",
"text": "Das BSI ist als Anbieter f\u00fcr die eigenen, zur Nutzung bereitgestellten Inhalte nach den allgemeinen Gesetzen verantwortlich. Nutzerinnen und Nutzer sind jedoch daf\u00fcr verantwortlich, die Verwendung und/oder die Umsetzung der mit den Inhalten bereitgestellten Informationen sorgf\u00e4ltig im Einzelfall zu pr\u00fcfen."
},
{
"category": "description",
"text": "Open Source vLLM ist eine Open-Source-Bibliothek f\u00fcr schnelle und effiziente Inferenz von Large Language Models (LLMs).",
"title": "Produktbeschreibung"
},
{
"category": "summary",
"text": "Ein Angreifer kann mehrere Schwachstellen in vllm ausnutzen, um Dateien zu manipulieren, Sicherheitsma\u00dfnahmen zu umgehen, vertrauliche Informationen offenzulegen oder einen Denial-of-Service-Zustand herbeizuf\u00fchren.",
"title": "Angriff"
},
{
"category": "general",
"text": "- Sonstiges\n- UNIX",
"title": "Betroffene Betriebssysteme"
}
],
"publisher": {
"category": "other",
"contact_details": "csaf-provider@cert-bund.de",
"name": "Bundesamt f\u00fcr Sicherheit in der Informationstechnik",
"namespace": "https://www.bsi.bund.de"
},
"references": [
{
"category": "self",
"summary": "WID-SEC-W-2026-0987 - CSAF Version",
"url": "https://wid.cert-bund.de/.well-known/csaf/white/2026/wid-sec-w-2026-0987.json"
},
{
"category": "self",
"summary": "WID-SEC-2026-0987 - Portal Version",
"url": "https://wid.cert-bund.de/portal/wid/securityadvisory?name=WID-SEC-2026-0987"
},
{
"category": "external",
"summary": "GitHub Security Advisory GHSA-pf3h-qjgv-vcpr vom 2026-04-07",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-pf3h-qjgv-vcpr"
},
{
"category": "external",
"summary": "GitHub Security Advisory GHSA-pq5c-rjhq-qp7p vom 2026-04-07",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-pq5c-rjhq-qp7p"
},
{
"category": "external",
"summary": "GitHub Security Advisory GHSA-3mwp-wvh9-7528 vom 2026-04-07",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-3mwp-wvh9-7528"
},
{
"category": "external",
"summary": "GitHub Security Advisory GHSA-6c4r-fmh3-7rh8 vom 2026-04-07",
"url": "https://github.com/vllm-project/vllm/security/advisories/GHSA-6c4r-fmh3-7rh8"
},
{
"category": "external",
"summary": "vllm releases vom 2026-04-07",
"url": "https://github.com/vllm-project/vllm/releases"
}
],
"source_lang": "en-US",
"title": "vllm: Mehrere Schwachstellen",
"tracking": {
"current_release_date": "2026-04-07T22:00:00.000+00:00",
"generator": {
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}
CVE-2026-34753 (GCVE-0-2026-34753)
Vulnerability from cvelistv5 – Published: 2026-04-06 15:36 – Updated: 2026-04-07 14:15
VLAI
EPSS
VEX
Title
vLLM affected by Server-Side Request Forgery (SSRF) in `download_bytes_from_url `
Summary
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.
Severity
5.4 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-07 14:15 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/vllm-project/vllm/security/adv… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.16.0, < 0.19.0
|
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CVE-2026-34755 (GCVE-0-2026-34755)
Vulnerability from cvelistv5 – Published: 2026-04-06 15:38 – Updated: 2026-08-25 12:05
VLAI
EPSS
VEX
Title
vLLM Affected by Denial of Service via Unbounded Frame Count in video/jpeg Base64 Processing
Summary
vLLM is an inference and serving engine for large language models (LLMs). From 0.7.0 to before 0.19.0, the VideoMediaIO.load_base64() method at vllm/multimodal/media/video.py splits video/jpeg data URLs by comma to extract individual JPEG frames, but does not enforce a frame count limit. The num_frames parameter (default: 32), which is enforced by the load_bytes() code path, is completely bypassed in the video/jpeg base64 path. An attacker can send a single API request containing thousands of comma-separated base64-encoded JPEG frames, causing the server to decode all frames into memory and crash with OOM. This vulnerability is fixed in 0.19.0.
Severity
6.5 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-06 18:36 UTC
CWE
- CWE-770 - Allocation of Resources Without Limits or Throttling
Assigner
References
12 references
| URL | Tags |
|---|---|
| https://github.com/vllm-project/vllm/security/adv… | x_refsource_CONFIRM |
| https://access.redhat.com/security/cve/CVE-2026-34755 | vdb-entryx_refsource_REDHAT |
| https://bugzilla.redhat.com/show_bug.cgi?id=2455403 | issue-trackingx_refsource_REDHAT |
| https://security.access.redhat.com/data/csaf/v2/v… | x_sadp-csaf-vex |
| https://access.redhat.com/errata/RHSA-2026:36005 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:36006 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:57380 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:57389 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:57390 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:57387 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:59151 | vendor-advisoryx_refsource_REDHAT |
| https://access.redhat.com/errata/RHSA-2026:59144 | vendor-advisoryx_refsource_REDHAT |
Impacted products
16 products
| Vendor | Product | Version | |
|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.7.0, < 0.19.0
|
|
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1782951012 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1782951244 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151769 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151840 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151771 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151774 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787310717 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787253912 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787253989 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787254059 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787243961 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787253774 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787244006 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server |
cpe:/a:redhat:ai_inference_server:3
|
|
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
|
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CVE-2026-34756 (GCVE-0-2026-34756)
Vulnerability from cvelistv5 – Published: 2026-04-06 15:40 – Updated: 2026-08-25 12:05
VLAI
EPSS
VEX
Title
vLLM Affected by Unauthenticated OOM Denial of Service via Unbounded `n` Parameter in OpenAI API Server
Summary
vLLM is an inference and serving engine for large language models (LLMs). From 0.1.0 to before 0.19.0, a Denial of Service vulnerability exists in the vLLM OpenAI-compatible API server. Due to the lack of an upper bound validation on the n parameter in the ChatCompletionRequest and CompletionRequest Pydantic models, an unauthenticated attacker can send a single HTTP request with an astronomically large n value. This completely blocks the Python asyncio event loop and causes immediate Out-Of-Memory crashes by allocating millions of request object copies in the heap before the request even reaches the scheduling queue. This vulnerability is fixed in 0.19.0.
Severity
6.5 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-07 14:16 UTC
CWE
Assigner
References
14 references
Impacted products
16 products
| Vendor | Product | Version | |
|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.1.0, < 0.19.0
|
|
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1782951012 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1782951244 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151769 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151840 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151771 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.4 |
Unaffected:
1787151774 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787310717 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787253912 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787253989 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787254059 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787243961 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787253774 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat Enterprise Linux AI 3.4 |
Unaffected:
1787244006 , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
|
| Red Hat | Red Hat AI Inference Server |
cpe:/a:redhat:ai_inference_server:3
|
|
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
|
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CVE-2026-34760 (GCVE-0-2026-34760)
Vulnerability from cvelistv5 – Published: 2026-04-02 18:59 – Updated: 2026-04-03 14:42
VLAI
EPSS
VEX
Title
vLLM: Downmix Implementation Differences as Attack Vectors Against Audio AI Models
Summary
vLLM is an inference and serving engine for large language models (LLMs). From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing (to_mono), while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy results in inconsistency between audio heard by humans (e.g., through headphones/regular speakers) and audio processed by AI models (Which infra via Librosa, such as vllm, transformer). This issue has been patched in version 0.18.0.
Severity
5.9 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-04-03 14:42 UTC
CWE
- CWE-20 - Improper Input Validation
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/vllm-project/vllm/security/adv… | x_refsource_CONFIRM |
| https://github.com/vllm-project/vllm/pull/37058 | x_refsource_MISC |
| https://github.com/vllm-project/vllm/commit/c7f98… | x_refsource_MISC |
| https://github.com/vllm-project/vllm/releases/tag… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.5.5, < 0.18.0
|
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Loading…
Trend slope:
-
(linear fit over daily sighting counts)
Show additional events:
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
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