CVE-2026-53134 (GCVE-0-2026-53134)
Vulnerability from cvelistv5 – Published: 2026-06-25 08:38 – Updated: 2026-09-08 08:50
VLAI
EPSS
VEX
Title
netfilter: nft_fib: fix stale stack leak via the OIFNAME register
Summary
In the Linux kernel, the following vulnerability has been resolved:
netfilter: nft_fib: fix stale stack leak via the OIFNAME register
For NFT_FIB_RESULT_OIFNAME the destination register is declared with
len = IFNAMSIZ (four 32-bit registers), but on the lookup-fail,
RTN_LOCAL and oif-mismatch paths nft_fib{4,6}_eval() only writes one
register via "*dest = 0". The remaining three registers are left as
whatever was on the stack in nft_do_chain()'s struct nft_regs, and a
downstream expression that loads the register span can leak that
uninitialised kernel stack to userspace.
The NFTA_FIB_F_PRESENT existence check has the same shape: it is only
meaningful for NFT_FIB_RESULT_OIF, yet it was accepted for any result type
while the eval stores a single byte via nft_reg_store8(), leaving the rest
of the declared span stale.
Fix both:
- replace the bare "*dest = 0" in the eval with nft_fib_store_result(),
which strscpy_pad()s the whole IFNAMSIZ for OIFNAME (and is already
used on the other early-return path), and
- restrict NFTA_FIB_F_PRESENT to NFT_FIB_RESULT_OIF and declare its
destination as a single u8, so the marked span matches the one byte
the eval writes.
Severity
5.5 (Medium)
Assigner
References
9 references
Impacted products
5 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
f6d0cbcf09c506b9b022df8f9d7693a7cec3c732 , < 6744e49fe51bfba26522acc2d0e9703cb41d8e50
(git)
Affected: f6d0cbcf09c506b9b022df8f9d7693a7cec3c732 , < eca18feed38b3377a2ec5d1f22af1170c55d0171 (git) Affected: f6d0cbcf09c506b9b022df8f9d7693a7cec3c732 , < d19ddef8c327a4773ff81f8e51027d1e0b4cf069 (git) Affected: f6d0cbcf09c506b9b022df8f9d7693a7cec3c732 , < eb8a8124484dbc3c2b543e207da39bbccb703d31 (git) Affected: f6d0cbcf09c506b9b022df8f9d7693a7cec3c732 , < 8c84885e9790823828bb8084736ea15769b1ac16 (git) Affected: f6d0cbcf09c506b9b022df8f9d7693a7cec3c732 , < 84d8f58cf28a0415413f43ba7148f7bacd4c1b6e (git) Affected: f6d0cbcf09c506b9b022df8f9d7693a7cec3c732 , < 3544210609f6d1db282bbdeca639104ef624c393 (git) Affected: f6d0cbcf09c506b9b022df8f9d7693a7cec3c732 , < ab185e0c4fb82dfba6fb86f8271e06f931d9c64c (git) |
guessed | |
| Linux | Linux |
Affected:
4.10
Unaffected: 0 , < 4.10 (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: 7.0.13 , ≤ 7.0.* (semver) Unaffected: 7.1 , ≤ * (original_commit_for_fix) |
guessed | |
| Siemens | SIMATIC S7-1500 CPU 1518-4 PN/DP MFP |
Affected:
V3.1.6 , < *
(custom)
|
guessed | |
| Siemens | SIMATIC S7-1500 CPU 1518F-4 PN/DP MFP |
Affected:
V3.1.6 , < *
(custom)
|
guessed | |
| Siemens | SIPLUS S7-1500 CPU 1518-4 PN/DP MFP |
Affected:
V3.1.6 , < *
(custom)
|
guessed |
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"redhat_vex": {
"aggregate_severity": "Moderate",
"current_release_date": "2026-07-07T17:06:07+00:00",
"cve": "CVE-2026-53134",
"id": "CVE-2026-53134",
"initial_release_date": "2026-06-25T00:00:00+00:00",
"product_status:known_affected": "232",
"product_status:known_not_affected": "42",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "kernel: netfilter: nft_fib: fix stale stack leak via the OIFNAME register",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-53134.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "low",
"current_release_date": "2026-09-10T16:57:58Z",
"cve": "CVE-2026-53134",
"id": "CVE-2026-53134",
"initial_release_date": "2026-06-26T02:12:31Z",
"product_status:first_fixed": "2",
"product_status:known_affected": "672",
"product_status:known_not_affected": "6",
"product_status:recommended": "273",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2026-53134",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2026-53134.json",
"version": "14"
}
}
}
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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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