CVE-2021-47168 (GCVE-0-2021-47168)
Vulnerability from cvelistv5 – Published: 2024-03-25 09:16 – Updated: 2026-08-05 08:46
VLAI
EPSS
VEX
Title
NFS: fix an incorrect limit in filelayout_decode_layout()
Summary
In the Linux kernel, the following vulnerability has been resolved:
NFS: fix an incorrect limit in filelayout_decode_layout()
The "sizeof(struct nfs_fh)" is two bytes too large and could lead to
memory corruption. It should be NFS_MAXFHSIZE because that's the size
of the ->data[] buffer.
I reversed the size of the arguments to put the variable on the left.
Severity
9.8 (Critical)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-09-10 15:54 UTC
Assigner
References
8 references
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| Linux | Linux |
Affected:
16b374ca439fb406e46e071f75428f5b033056f8 , < 9d280ab53df1d4a1043bd7a9e7c6a2f9cfbfe040
(git)
Affected: 16b374ca439fb406e46e071f75428f5b033056f8 , < b287521e9e94bb342ebe5fd8c3fd7db9aef4e6f1 (git) Affected: 16b374ca439fb406e46e071f75428f5b033056f8 , < f299522eda1566cbfbae4b15c82970fc41b03714 (git) Affected: 16b374ca439fb406e46e071f75428f5b033056f8 , < 945ebef997227ca8c20bad7f8a8358c8ee57a84a (git) Affected: 16b374ca439fb406e46e071f75428f5b033056f8 , < e411df81cd862ef3d5b878120b2a2fef0ca9cdb1 (git) Affected: 16b374ca439fb406e46e071f75428f5b033056f8 , < 9b367fe770b1b80d7bf64ed0d177544a44405f6e (git) Affected: 16b374ca439fb406e46e071f75428f5b033056f8 , < d34fb628f6ef522f996205a9e578216bbee09e84 (git) Affected: 16b374ca439fb406e46e071f75428f5b033056f8 , < 769b01ea68b6c49dc3cde6adf7e53927dacbd3a8 (git) |
|
| Linux | Linux |
Affected:
2.6.37
Unaffected: 0 , < 2.6.37 (semver) Unaffected: 4.4.271 , ≤ 4.4.* (semver) Unaffected: 4.9.271 , ≤ 4.9.* (semver) Unaffected: 4.14.235 , ≤ 4.14.* (semver) Unaffected: 4.19.193 , ≤ 4.19.* (semver) Unaffected: 5.4.124 , ≤ 5.4.* (semver) Unaffected: 5.10.42 , ≤ 5.10.* (semver) Unaffected: 5.12.9 , ≤ 5.12.* (semver) Unaffected: 5.13 , ≤ * (original_commit_for_fix) |
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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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