GHSA-QX3X-JWR3-P8VV
Vulnerability from github – Published: 2026-08-28 09:31 – Updated: 2026-08-28 09:31
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
bpf: Disable xfrm_decode_session hook attachment
BPF LSM programs can currently attach to xfrm_decode_session(). That hook may return an error, but security_skb_classify_flow() calls it from a void path and triggers BUG_ON() if an error is returned.
Disable BPF attachment to the hook to prevent a BPF LSM program from turning packet classification into a full panic.
{
"affected": [],
"aliases": [
"CVE-2026-80669"
],
"database_specific": {
"cwe_ids": [],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-08-28T08:16:52Z",
"severity": null
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nbpf: Disable xfrm_decode_session hook attachment\n\nBPF LSM programs can currently attach to xfrm_decode_session(). That\nhook may return an error, but security_skb_classify_flow() calls it\nfrom a void path and triggers BUG_ON() if an error is returned.\n\nDisable BPF attachment to the hook to prevent a BPF LSM program from\nturning packet classification into a full panic.",
"id": "GHSA-qx3x-jwr3-p8vv",
"modified": "2026-08-28T09:31:48Z",
"published": "2026-08-28T09:31:48Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-80669"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/12091470c6b4c1c14b2de12dcbae2ada6cb6d20b"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/1bb3b6a5c3c5cc814eae4ff0212fdced36f8bce9"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/49fa1be621dde8f526d36e5791aded8fafda1e9c"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/4ae780d173ef40d4f7cb76935dc2d55fbf380009"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/6b44c6660aa1c9b843e450a623ac4a8484919dce"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/aa265d47308775c5501073b08133623bcb5421f9"
}
],
"schema_version": "1.4.0",
"severity": []
}
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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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