CVE-2026-56210 (GCVE-0-2026-56210)
Vulnerability from cvelistv5 – Published: 2026-06-19 16:28 – Updated: 2026-09-24 08:03
VLAI
EPSS
VEX
Title
Libaom: libaom: heap-buffer-overflow read via missing bounds check in ctrl_set_layer_id
Summary
A heap-buffer-overflow read vulnerability was found in libaom, the reference AV1 codec implementation. A missing bounds check in the SVC (Scalable Video Coding) layer ID control function allows setting a spatial_layer_id exceeding the configured number of layers. This causes an out-of-bounds heap read of approximately 40,728 bytes when computing a layer context array index. An attacker who can influence SVC encoder parameters in a network-facing service could exploit this for information disclosure (heap content leak) or denial of service (segmentation fault from hitting unmapped memory).
Severity
7.1 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-06-22 18:22 UTC
CWE
- CWE-125 - Out-of-bounds Read
Assigner
References
42 references
Impacted products
41 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Red Hat | Red Hat Enterprise Linux AI 3.3 for RHEL 9 |
Unaffected:
0:3.14.0-1.el9ai , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.3::el9 |
||
| Red Hat | Red Hat Enterprise Linux AI 3.4 for RHEL 9 |
Unaffected:
0:3.14.0-1.el9ai , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.4::el9 |
||
| Red Hat | Red Hat Enterprise Linux AI 3.5 for RHEL 9 |
Unaffected:
0:3.14.0-1.el9ai , < *
(rpm)
cpe:/a:redhat:enterprise_linux_ai:3.5::el9 |
||
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1787860580 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
||
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1787772157 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
||
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1787884873 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
||
| Red Hat | Red Hat AI Inference Server 3.3 |
Unaffected:
1789508287 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.3::el9 |
||
| Red Hat | Red Hat AI Inference Server 3.3 |
Unaffected:
1789502493 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.3::el9 |
||
| Red Hat | Red Hat AI Inference Server 3.3 |
Unaffected:
1789504371 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.3::el9 |
||
| Red Hat | Red Hat AI Inference Server 3.3 |
Unaffected:
1789582776 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.3::el9 |
||
| Red Hat | Red Hat Hardened Images |
Unaffected:
3.14.0-0.1.hum1 , < *
(rpm)
cpe:/a:redhat:hummingbird:1 |
||
| Red Hat | Red Hat OpenShift AI 3.3 |
Unaffected:
1789137552 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.3::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.3 |
Unaffected:
1789137548 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.3::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.3 |
Unaffected:
1789137636 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.3::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.3 |
Unaffected:
1789137641 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.3::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.3 |
Unaffected:
1789137607 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.3::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1786611800 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787076778 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787077779 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1787076481 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1788197680 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1788197530 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1788197632 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1788197747 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1788197549 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1788199622 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1788197744 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.4 |
Unaffected:
1788197677 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.4::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.5 |
Unaffected:
1788198404 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.5::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.5 |
Unaffected:
1788198407 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.5::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.5 |
Unaffected:
1788197492 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.5::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.5 |
Unaffected:
1788197542 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.5::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.5 |
Unaffected:
1788197975 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.5::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.5 |
Unaffected:
1788197512 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.5::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.5 |
Unaffected:
1788197688 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.5::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.5 |
Unaffected:
1788197385 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.5::el9 |
||
| Red Hat | Red Hat OpenShift AI 3.5 |
Unaffected:
1788198016 , < *
(rpm)
cpe:/a:redhat:openshift_ai:3.5::el9 |
||
| Red Hat | Red Hat AI Inference Server |
cpe:/a:redhat:ai_inference_server:3
|
||
| Red Hat | Red Hat Enterprise Linux 10 |
cpe:/o:redhat:enterprise_linux:10
|
||
| Red Hat | Red Hat Enterprise Linux 9 |
cpe:/o:redhat:enterprise_linux:9
|
||
| Red Hat | Red Hat OpenShift AI (RHOAI) |
cpe:/a:redhat:openshift_ai
|
Date Public
2026-06-19 00:00
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"credits": [
{
"lang": "en",
"value": "Red Hat would like to thank The FuzzAnything Team (FuzzAnything) for reporting this issue."
}
],
"datePublic": "2026-06-19T00:00:00.000Z",
"descriptions": [
{
"lang": "en",
"value": "A heap-buffer-overflow read vulnerability was found in libaom, the reference AV1 codec implementation. A missing bounds check in the SVC (Scalable Video Coding) layer ID control function allows setting a spatial_layer_id exceeding the configured number of layers. This causes an out-of-bounds heap read of approximately 40,728 bytes when computing a layer context array index. An attacker who can influence SVC encoder parameters in a network-facing service could exploit this for information disclosure (heap content leak) or denial of service (segmentation fault from hitting unmapped memory)."
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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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