FKIE_CVE-2026-92515
Vulnerability from fkie_nvd - Published: 2026-09-17 17:17 - Updated: 2026-09-17 17:17
Severity
Summary
In the Linux kernel, the following vulnerability has been resolved:
bpf: Preserve unique-field state across nested structs
btf_find_struct_field() initializes a fresh seen mask for every recursive
descent. Unique special fields in different levels of the same aggregate
therefore do not see one another. The duplicate fields can reach
btf_parse_fields(), where they trigger an invariant WARN_ON_ONCE(). A
crafted user BTF can consequently trigger the warning before map creation
checks capabilities.
Initialize the seen mask once in btf_find_field() and pass the same pointer
through struct, datasec, and nested-struct walks. This gives the entire field
traversal one shared uniqueness state.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"defaultStatus": "unaffected",
"product": "Linux",
"programFiles": [
"kernel/bpf/btf.c"
],
"repo": "https://git.kernel.org/pub/scm/linux/kernel/git/stable/linux.git",
"vendor": "Linux",
"versions": [
{
"lessThan": "cec97102d4147e18568c93946014c4c8ebf86fcf",
"status": "affected",
"version": "64e8ee814819f21beeeda00d4119221443d77992",
"versionType": "git"
},
{
"lessThan": "28d5f20ed022ade0fb0bfbf50b78fec782083175",
"status": "affected",
"version": "64e8ee814819f21beeeda00d4119221443d77992",
"versionType": "git"
},
{
"lessThan": "373a1e9366e711b89bc5e45bd6edf33ef62402cd",
"status": "affected",
"version": "64e8ee814819f21beeeda00d4119221443d77992",
"versionType": "git"
},
{
"lessThan": "f08619f060468076e4acbdc10e0713af20d60e65",
"status": "affected",
"version": "64e8ee814819f21beeeda00d4119221443d77992",
"versionType": "git"
}
]
},
{
"defaultStatus": "affected",
"product": "Linux",
"programFiles": [
"kernel/bpf/btf.c"
],
"repo": "https://git.kernel.org/pub/scm/linux/kernel/git/stable/linux.git",
"vendor": "Linux",
"versions": [
{
"status": "affected",
"version": "6.11"
},
{
"lessThan": "6.11",
"status": "unaffected",
"version": "0",
"versionType": "semver"
},
{
"lessThanOrEqual": "6.12.*",
"status": "unaffected",
"version": "6.12.110",
"versionType": "semver"
},
{
"lessThanOrEqual": "6.18.*",
"status": "unaffected",
"version": "6.18.52",
"versionType": "semver"
},
{
"lessThanOrEqual": "7.2.*",
"status": "unaffected",
"version": "7.2.6",
"versionType": "semver"
},
{
"lessThanOrEqual": "*",
"status": "unaffected",
"version": "7.3-rc1",
"versionType": "original_commit_for_fix"
}
]
}
],
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "In the Linux kernel, the following vulnerability has been resolved:\n\nbpf: Preserve unique-field state across nested structs\n\nbtf_find_struct_field() initializes a fresh seen mask for every recursive\ndescent. Unique special fields in different levels of the same aggregate\ntherefore do not see one another. The duplicate fields can reach\nbtf_parse_fields(), where they trigger an invariant WARN_ON_ONCE(). A\ncrafted user BTF can consequently trigger the warning before map creation\nchecks capabilities.\n\nInitialize the seen mask once in btf_find_field() and pass the same pointer\nthrough struct, datasec, and nested-struct walks. This gives the entire field\ntraversal one shared uniqueness state."
}
],
"id": "CVE-2026-92515",
"lastModified": "2026-09-17T17:17:54.360",
"metrics": {},
"published": "2026-09-17T17:17:54.360",
"references": [
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"url": "https://git.kernel.org/stable/c/28d5f20ed022ade0fb0bfbf50b78fec782083175"
},
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"url": "https://git.kernel.org/stable/c/373a1e9366e711b89bc5e45bd6edf33ef62402cd"
},
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"url": "https://git.kernel.org/stable/c/cec97102d4147e18568c93946014c4c8ebf86fcf"
},
{
"source": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"url": "https://git.kernel.org/stable/c/f08619f060468076e4acbdc10e0713af20d60e65"
}
],
"sourceIdentifier": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"vulnStatus": "Received"
}
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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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