CVE-2022-50140 (GCVE-0-2022-50140)
Vulnerability from cvelistv5 – Published: 2025-06-18 11:03 – Updated: 2026-05-11 19:13
VLAI
EPSS
VEX
Title
memstick/ms_block: Fix a memory leak
Summary
In the Linux kernel, the following vulnerability has been resolved:
memstick/ms_block: Fix a memory leak
'erased_blocks_bitmap' is never freed. As it is allocated at the same time
as 'used_blocks_bitmap', it is likely that it should be freed also at the
same time.
Add the corresponding bitmap_free() in msb_data_clear().
Severity
5.5 (Medium)
Assigner
References
8 references
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
0ab30494bc4f3bc1ea4659b7c5d97c5218554a63 , < efd675246aec045507b9425c67b548cc2d782d8f
(git)
Affected: 0ab30494bc4f3bc1ea4659b7c5d97c5218554a63 , < 37958980eb4cd71ae594ace093c11b6a91e165e8 (git) Affected: 0ab30494bc4f3bc1ea4659b7c5d97c5218554a63 , < 9d8b911fe3c3ed788c66edba7c90e32a4a7a5f53 (git) Affected: 0ab30494bc4f3bc1ea4659b7c5d97c5218554a63 , < 9260a154b3b5e387dbceec7c0ac441470646bc6f (git) Affected: 0ab30494bc4f3bc1ea4659b7c5d97c5218554a63 , < 961d7d12080fe70847f944d656e36cd0dd0214ba (git) Affected: 0ab30494bc4f3bc1ea4659b7c5d97c5218554a63 , < 16e07966638717416abf45393d6a80a5a1034429 (git) Affected: 0ab30494bc4f3bc1ea4659b7c5d97c5218554a63 , < 39be95d1ff7b44c1e969af72ba9da7332dfcc1da (git) Affected: 0ab30494bc4f3bc1ea4659b7c5d97c5218554a63 , < 54eb7a55be6779c4d0c25eaf5056498a28595049 (git) |
guessed | |
| Linux | Linux |
Affected:
3.12
Unaffected: 0 , < 3.12 (semver) Unaffected: 4.14.291 , ≤ 4.14.* (semver) Unaffected: 4.19.256 , ≤ 4.19.* (semver) Unaffected: 5.4.211 , ≤ 5.4.* (semver) Unaffected: 5.10.137 , ≤ 5.10.* (semver) Unaffected: 5.15.61 , ≤ 5.15.* (semver) Unaffected: 5.18.18 , ≤ 5.18.* (semver) Unaffected: 5.19.2 , ≤ 5.19.* (semver) Unaffected: 6.0 , ≤ * (original_commit_for_fix) |
guessed |
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"redhat_vex": {
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"cve": "CVE-2022-50140",
"id": "CVE-2022-50140",
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"title": "kernel: memstick/ms_block: Fix a memory leak",
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"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2022-50140.json",
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}
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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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