GHSA-WFQW-582V-82VH
Vulnerability from github – Published: 2026-08-15 06:32 – Updated: 2026-08-15 06:32
VLAI
Details
In the Linux kernel, the following vulnerability has been resolved:
net/sched: cls_flow: Dont expose folded kernel pointers
The flow classifier falls back to addr_fold() for fields that are missing from packet headers. In map mode, userspace controls mask, xor, rshift, addend and divisor, and can observe the resulting classid through class statistics. This allows a tc classifier in a user/network namespace to recover the 32-bit folded value of skb->sk, skb_dst() or skb_nfct().
Align with standard kernel practices for pointer hashing and replace the XOR folding with a keyed siphash (which is cryptographically secure)
{
"affected": [],
"aliases": [
"CVE-2026-74290"
],
"database_specific": {
"cwe_ids": [],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-08-15T06:22:28Z",
"severity": null
},
"details": "In the Linux kernel, the following vulnerability has been resolved:\n\nnet/sched: cls_flow: Dont expose folded kernel pointers\n\nThe flow classifier falls back to addr_fold() for fields that are missing\nfrom packet headers. In map mode, userspace controls mask, xor, rshift,\naddend and divisor, and can observe the resulting classid through class\nstatistics. This allows a tc classifier in a user/network namespace to\nrecover the 32-bit folded value of skb-\u003esk, skb_dst() or skb_nfct().\n\nAlign with standard kernel practices for pointer hashing and replace the\nXOR folding with a keyed siphash (which is cryptographically secure)",
"id": "GHSA-wfqw-582v-82vh",
"modified": "2026-08-15T06:32:28Z",
"published": "2026-08-15T06:32:28Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-74290"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/0a8b5b74f0e6b6b9ce453bcfa4baa502c4c7577a"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/19f2ecf8ea564562c7e7a919cf38068dd9aa1c96"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/3d054001860270748405a3f9270c5fa0bf7ffc19"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/6151159618198675e01c391676e579738286c135"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/f294fc71c4a0fa4964f6428a1b4e7929c1d83125"
},
{
"type": "WEB",
"url": "https://git.kernel.org/stable/c/fb31fbe51c233f3bf47021121f63d2e42cac2d08"
}
],
"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.
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.
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