CVE-2025-21635 (GCVE-0-2025-21635)
Vulnerability from cvelistv5 – Published: 2025-01-19 10:17 – Updated: 2026-05-11 21:03
VLAI
EPSS
VEX
Title
rds: sysctl: rds_tcp_{rcv,snd}buf: avoid using current->nsproxy
Summary
In the Linux kernel, the following vulnerability has been resolved:
rds: sysctl: rds_tcp_{rcv,snd}buf: avoid using current->nsproxy
As mentioned in a previous commit of this series, using the 'net'
structure via 'current' is not recommended for different reasons:
- Inconsistency: getting info from the reader's/writer's netns vs only
from the opener's netns.
- current->nsproxy can be NULL in some cases, resulting in an 'Oops'
(null-ptr-deref), e.g. when the current task is exiting, as spotted by
syzbot [1] using acct(2).
The per-netns structure can be obtained from the table->data using
container_of(), then the 'net' one can be retrieved from the listen
socket (if available).
Severity
5.5 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-10-01 19:54 UTC
CWE
- CWE-476 - NULL Pointer Dereference
Assigner
References
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
c6a58ffed53612be86b758df1cdb0b0f4305e9cb , < de8d6de0ee27be4b2b1e5b06f04aeacbabbba492
(git)
Affected: c6a58ffed53612be86b758df1cdb0b0f4305e9cb , < 7f5611cbc4871c7fb1ad36c2e5a9edad63dca95c (git) |
guessed | |
| Linux | Linux |
Affected:
4.6
Unaffected: 0 , < 4.6 (semver) Unaffected: 6.12.10 , ≤ 6.12.* (semver) Unaffected: 6.13 , ≤ * (original_commit_for_fix) |
guessed |
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],
"title": "rds: sysctl: rds_tcp_{rcv,snd}buf: avoid using current-\u003ensproxy",
"x_generator": {
"engine": "bippy-1.2.0"
}
}
},
"cveMetadata": {
"assignerOrgId": "416baaa9-dc9f-4396-8d5f-8c081fb06d67",
"assignerShortName": "Linux",
"cveId": "CVE-2025-21635",
"datePublished": "2025-01-19T10:17:53.832Z",
"dateReserved": "2024-12-29T08:45:45.726Z",
"dateUpdated": "2025-10-01T19:57:18.232Z",
"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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