CVE-2026-45850 (GCVE-0-2026-45850)
Vulnerability from cvelistv5 – Published: 2026-05-27 12:15 – Updated: 2026-06-19 11:58
VLAI
EPSS
VEX
Title
ipvs: skip ipv6 extension headers for csum checks
Summary
In the Linux kernel, the following vulnerability has been resolved:
ipvs: skip ipv6 extension headers for csum checks
Protocol checksum validation fails for IPv6 if there are extension
headers before the protocol header. iph->len already contains its
offset, so use it to fix the problem.
Severity
5.5 (Medium)
Assigner
References
8 references
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
0bbdd42b7efa66685b6d74701bcde3a596a3a59d , < 0bf92a90bf05ecafe52e92d5bc15a585021a64ac
(git)
Affected: 0bbdd42b7efa66685b6d74701bcde3a596a3a59d , < 54add3b7d3c154ca89ef5bac2582b0ed1a3a15d5 (git) Affected: 0bbdd42b7efa66685b6d74701bcde3a596a3a59d , < 768f665f3685b455ed686370ed7ccb852a125a3b (git) Affected: 0bbdd42b7efa66685b6d74701bcde3a596a3a59d , < 9aa7edc1347b98774e5167ca34e5b8aa6083bde7 (git) Affected: 0bbdd42b7efa66685b6d74701bcde3a596a3a59d , < d643c1ec80b70508f54dac12179e36920e2c00de (git) Affected: 0bbdd42b7efa66685b6d74701bcde3a596a3a59d , < cdce1e797addab72393c0dfee31aaca41ef7d937 (git) Affected: 0bbdd42b7efa66685b6d74701bcde3a596a3a59d , < a3ca27762ce8476b4fbf9b2a8f5cb74c38e483e4 (git) Affected: 0bbdd42b7efa66685b6d74701bcde3a596a3a59d , < 05cfe9863ef049d98141dc2969eefde72fb07625 (git) |
guessed | |
| Linux | Linux |
Affected:
2.6.28
Unaffected: 0 , < 2.6.28 (semver) Unaffected: 5.10.259 , ≤ 5.10.* (semver) Unaffected: 5.15.210 , ≤ 5.15.* (semver) Unaffected: 6.1.176 , ≤ 6.1.* (semver) Unaffected: 6.6.143 , ≤ 6.6.* (semver) Unaffected: 6.12.94 , ≤ 6.12.* (semver) Unaffected: 6.18.36 , ≤ 6.18.* (semver) Unaffected: 6.19.4 , ≤ 6.19.* (semver) Unaffected: 7.0 , ≤ * (original_commit_for_fix) |
guessed |
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}
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"status": "interim",
"title": "SUSE CVE CVE-2026-45850",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2026-45850.json",
"version": "7"
}
}
}
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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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