CVE-2022-48742 (GCVE-0-2022-48742)
Vulnerability from cvelistv5 – Published: 2024-06-20 11:13 – Updated: 2026-08-05 08:52
VLAI
EPSS
VEX
Title
rtnetlink: make sure to refresh master_dev/m_ops in __rtnl_newlink()
Summary
In the Linux kernel, the following vulnerability has been resolved:
rtnetlink: make sure to refresh master_dev/m_ops in __rtnl_newlink()
While looking at one unrelated syzbot bug, I found the replay logic
in __rtnl_newlink() to potentially trigger use-after-free.
It is better to clear master_dev and m_ops inside the loop,
in case we have to replay it.
Severity
7.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-09-10 17:10 UTC
Assigner
References
8 references
Impacted products
2 products
| Vendor | Product | Version | |
|---|---|---|---|
| Linux | Linux |
Affected:
ba7d49b1f0f8e5f24294a880ed576964059af5ef , < 2cf180360d66bd657e606c1217e0e668e6faa303
(git)
Affected: ba7d49b1f0f8e5f24294a880ed576964059af5ef , < 7d9211678c0f0624f74cdff36117ab8316697bb8 (git) Affected: ba7d49b1f0f8e5f24294a880ed576964059af5ef , < a01e60a1ec6bef9be471fb7182a33c6d6f124e93 (git) Affected: ba7d49b1f0f8e5f24294a880ed576964059af5ef , < bd43771ee9759dd9dfae946bff190e2c5a120de5 (git) Affected: ba7d49b1f0f8e5f24294a880ed576964059af5ef , < 3bbe2019dd12b8d13671ee6cda055d49637b4c39 (git) Affected: ba7d49b1f0f8e5f24294a880ed576964059af5ef , < def5e7070079b2a214b3b1a2fbec623e6fbfe34a (git) Affected: ba7d49b1f0f8e5f24294a880ed576964059af5ef , < 36a9a0aee881940476b254e0352581401b23f210 (git) Affected: ba7d49b1f0f8e5f24294a880ed576964059af5ef , < c6f6f2444bdbe0079e41914a35081530d0409963 (git) |
|
| Linux | Linux |
Affected:
3.14
Unaffected: 0 , < 3.14 (semver) Unaffected: 4.9.300 , ≤ 4.9.* (semver) Unaffected: 4.14.265 , ≤ 4.14.* (semver) Unaffected: 4.19.228 , ≤ 4.19.* (semver) Unaffected: 5.4.177 , ≤ 5.4.* (semver) Unaffected: 5.10.97 , ≤ 5.10.* (semver) Unaffected: 5.15.20 , ≤ 5.15.* (semver) Unaffected: 5.16.6 , ≤ 5.16.* (semver) Unaffected: 5.17 , ≤ * (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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