CVE-2024-1085 (GCVE-0-2024-1085)
Vulnerability from cvelistv5 – Published: 2024-01-31 12:14 – Updated: 2025-05-29 15:03
VLAI
EPSS
VEX
Title
Use-after-free in Linux kernel's netfilter: nf_tables component
Summary
A use-after-free vulnerability in the Linux kernel's netfilter: nf_tables component can be exploited to achieve local privilege escalation.
The nft_setelem_catchall_deactivate() function checks whether the catch-all set element is active in the current generation instead of the next generation before freeing it, but only flags it inactive in the next generation, making it possible to free the element multiple times, leading to a double free vulnerability.
We recommend upgrading past commit b1db244ffd041a49ecc9618e8feb6b5c1afcdaa7.
Severity
7.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-05-08 15:46 UTC
CWE
- CWE-416 - Use After Free
Assigner
References
2 references
Impacted products
Date Public
2024-01-17 11:02
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"lang": "en",
"value": "GENERAL"
}
]
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-416",
"description": "CWE-416 Use After Free",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2024-01-31T12:14:32.429Z",
"orgId": "14ed7db2-1595-443d-9d34-6215bf890778",
"shortName": "Google"
},
"references": [
{
"tags": [
"patch"
],
"url": "https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/commit/?id=b1db244ffd041a49ecc9618e8feb6b5c1afcdaa7"
},
{
"url": "https://kernel.dance/b1db244ffd041a49ecc9618e8feb6b5c1afcdaa7"
}
],
"source": {
"discovery": "EXTERNAL"
},
"title": "Use-after-free in Linux kernel\u0027s netfilter: nf_tables component",
"x_generator": {
"engine": "Vulnogram 0.1.0-dev"
}
}
},
"cveMetadata": {
"assignerOrgId": "14ed7db2-1595-443d-9d34-6215bf890778",
"assignerShortName": "Google",
"cveId": "CVE-2024-1085",
"datePublished": "2024-01-31T12:14:32.429Z",
"dateReserved": "2024-01-30T20:04:08.623Z",
"dateUpdated": "2025-05-29T15:03:59.158Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1"
}
}
}
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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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