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EUVD-2026-337493
European Vulnerability Database identifier assigned by ENISAReserved
2026-10-02 07:52
Assigner
ENISA
Alias of
a CVE record, shown under related vulnerabilities.
This identifier carries no description, severity or references of its own:
they belong to that CVE.
{
"assigner": "ENISA",
"date_reserved": "2026-10-02T07:52:34.931106+00:00",
"id": "EUVD-2026-337493"
}
CVE-2026-22778 (GCVE-0-2026-22778)
Vulnerability from cvelistv5 – Published: 2026-02-02 21:09 – Updated: 2026-07-15 01:19
VLAI
EPSS
VEX
Title
vLLM leaks a heap address when PIL throws an error
Summary
vLLM is an inference and serving engine for large language models (LLMs). From 0.8.3 to before 0.14.1, when an invalid image is sent to vLLM's multimodal endpoint, PIL throws an error. vLLM returns this error to the client, leaking a heap address. With this leak, we reduce ASLR from 4 billion guesses to ~8 guesses. This vulnerability can be chained a heap overflow with JPEG2000 decoder in OpenCV/FFmpeg to achieve remote code execution. This vulnerability is fixed in 0.14.1.
Severity
9.8 (Critical)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-02-03 15:40 UTC
CWE
Assigner
References
15 references
Impacted products
15 products
| Vendor | Product | Version | |
|---|---|---|---|
| vllm-project | vllm |
Affected:
>= 0.8.3, < 0.14.1
|
|
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1772160593 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1772160625 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.3 |
Unaffected:
1782352919 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.3::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.3 |
Unaffected:
1782353093 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.3::el9 |
|
| Red Hat | Red Hat AI Inference Server 3.3 |
Unaffected:
1782352847 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.3::el9 |
|
| Red Hat | Red Hat OpenShift AI 2.25 |
Unaffected:
1772093436 , < *
(rpm)
cpe:/a:redhat:openshift_ai:2.25::el9 |
|
| Red Hat | Red Hat OpenShift AI 2.25 |
Unaffected:
1772093276 , < *
(rpm)
cpe:/a:redhat:openshift_ai:2.25::el9 |
|
| Red Hat | Red Hat OpenShift AI 2.25 |
Unaffected:
1772093278 , < *
(rpm)
cpe:/a:redhat:openshift_ai:2.25::el9 |
|
| Red Hat | Red Hat OpenShift AI 2.25 |
Unaffected:
1772093237 , < *
(rpm)
cpe:/a:redhat:openshift_ai:2.25::el9 |
|
| Red Hat | Red Hat OpenShift AI 3.3 |
Unaffected:
1778264363 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.3::el9 |
|
| Red Hat | Red Hat OpenShift AI 3.3 |
Unaffected:
1770956034 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.3::el9 |
|
| Red Hat | Red Hat AI Inference Server |
cpe:/a:redhat:ai_inference_server:3
|
|
| Red Hat | Red Hat Enterprise Linux AI (RHEL AI) 3 |
cpe:/a:redhat:enterprise_linux_ai:3
|
|
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
|
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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