CVE-2026-43296 (GCVE-0-2026-43296)
Vulnerability from cvelistv5 – Published: 2026-05-08 13:11 – Updated: 2026-08-05 12:27
VLAI
EPSS
VEX
Title
octeontx2-af: Workaround SQM/PSE stalls by disabling sticky
Summary
In the Linux kernel, the following vulnerability has been resolved:
octeontx2-af: Workaround SQM/PSE stalls by disabling sticky
NIX SQ manager sticky mode is known to cause stalls when multiple SQs
share an SMQ and transmit concurrently. Additionally, PSE may deadlock
on transitions between sticky and non-sticky transmissions. There is
also a credit drop issue observed when certain condition clocks are
gated.
work around these hardware errata by:
- Disabling SQM sticky operation:
- Clear TM6 (bit 15)
- Clear TM11 (bit 14)
- Disabling sticky → non-sticky transition path that can deadlock PSE:
- Clear TM5 (bit 23)
- Preventing credit drops by keeping the control-flow clock enabled:
- Set TM9 (bit 21)
These changes are applied via NIX_AF_SQM_DBG_CTL_STATUS. With this
configuration the SQM/PSE maintain forward progress under load without
credit loss, at the cost of disabling sticky optimizations.
Severity
7.5 (High)
Assigner
References
8 references
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| Linux | Linux |
Affected:
5d9b976d4480dc0dcfa3719b645636d2f0f9f156 , < 9a3fd301329474f449e75f86d8a4f6b9c603fd6c
(git)
Affected: 5d9b976d4480dc0dcfa3719b645636d2f0f9f156 , < d0b3c8a80336029d9356f429151eb27922d80a3c (git) Affected: 5d9b976d4480dc0dcfa3719b645636d2f0f9f156 , < 36cc5a5e0178d5fb79e04173b8aa623b0108819a (git) Affected: 5d9b976d4480dc0dcfa3719b645636d2f0f9f156 , < d9b549b6951ba178ec14339a031cae65f4e43fe1 (git) Affected: 5d9b976d4480dc0dcfa3719b645636d2f0f9f156 , < cec2ceb35ce7bc874c43812bb39200d6cf691b87 (git) Affected: 5d9b976d4480dc0dcfa3719b645636d2f0f9f156 , < 8052d0587fb14b85539c3a14a226586c0c3d6b4c (git) Affected: 5d9b976d4480dc0dcfa3719b645636d2f0f9f156 , < b7eba260a34e854e2487b8363c11976f082df00d (git) Affected: 5d9b976d4480dc0dcfa3719b645636d2f0f9f156 , < 70e9a5760abfb6338d63994d4de6b0778ec795d6 (git) |
|
| Linux | Linux |
Affected:
5.5
Unaffected: 0 , < 5.5 (semver) Unaffected: 5.10.252 , ≤ 5.10.* (semver) Unaffected: 5.15.202 , ≤ 5.15.* (semver) Unaffected: 6.1.165 , ≤ 6.1.* (semver) Unaffected: 6.6.128 , ≤ 6.6.* (semver) Unaffected: 6.12.75 , ≤ 6.12.* (semver) Unaffected: 6.18.16 , ≤ 6.18.* (semver) Unaffected: 6.19.6 , ≤ 6.19.* (semver) Unaffected: 7.0 , ≤ * (original_commit_for_fix) |
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"url": "https://git.kernel.org/stable/c/cec2ceb35ce7bc874c43812bb39200d6cf691b87"
},
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"tags": [
"Patch"
],
"url": "https://git.kernel.org/stable/c/d0b3c8a80336029d9356f429151eb27922d80a3c"
},
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"tags": [
"Patch"
],
"url": "https://git.kernel.org/stable/c/d9b549b6951ba178ec14339a031cae65f4e43fe1"
}
],
"sourceIdentifier": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"vulnStatus": "Analyzed",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-667"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
},
"redhat_vex": {
"aggregate_severity": "Moderate",
"current_release_date": "2026-07-02T19:37:26+00:00",
"cve": "CVE-2026-43296",
"id": "CVE-2026-43296",
"initial_release_date": "2026-05-08T00:00:00+00:00",
"product_status:known_affected": "184",
"product_status:known_not_affected": "90",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "kernel: octeontx2-af: Workaround SQM/PSE stalls by disabling sticky",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-43296.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "moderate",
"current_release_date": "2026-10-01T01:20:40Z",
"cve": "CVE-2026-43296",
"id": "CVE-2026-43296",
"initial_release_date": "2026-05-12T03:31:09Z",
"product_status:known_affected": "530",
"product_status:known_not_affected": "190",
"product_status:recommended": "252",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2026-43296",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2026-43296.json",
"version": "13"
}
}
}
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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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