CVE-2022-2207 (GCVE-0-2022-2207)
Vulnerability from cvelistv5 – Published: 2022-06-27 00:00 – Updated: 2024-08-03 00:32
VLAI
EPSS
VEX
Title
Heap-based Buffer Overflow in vim/vim
Summary
Heap-based Buffer Overflow in GitHub repository vim/vim prior to 8.2.
Severity
7.8 (High)
CWE
- CWE-122 - Heap-based Buffer Overflow
Assigner
References
6 references
| URL | Tags |
|---|---|
| https://huntr.dev/bounties/05bc6051-4dc3-483b-ae5… | |
| https://github.com/vim/vim/commit/0971c7a4e537ea1… | |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisory |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisory |
| https://security.gentoo.org/glsa/202208-32 | vendor-advisory |
| https://security.gentoo.org/glsa/202305-16 | vendor-advisory |
Impacted products
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"id": "CVE-2022-2207",
"lastModified": "2024-11-21T07:00:32.190",
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"id": "msrc_CVE-2022-2207",
"initial_release_date": "2022-06-02T00:00:00.000Z",
"product_status:fixed": "2",
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"source": "Microsoft CSAF VEX",
"status": "final",
"title": "Heap-based Buffer Overflow in vim/vim",
"url": "https://msrc.microsoft.com/csaf/vex/2022/msrc_cve-2022-2207.json",
"version": "1"
},
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"redhat_vex": {
"aggregate_severity": "Low",
"current_release_date": "2025-11-21T15:31:56+00:00",
"cve": "CVE-2022-2207",
"id": "CVE-2022-2207",
"initial_release_date": "2022-06-27T00:00:00+00:00",
"product_status:known_affected": "24",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "vim: heap-based buffer overflow in function ins_bs",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2022/cve-2022-2207.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "moderate",
"current_release_date": "2026-09-02T02:14:59Z",
"cve": "CVE-2022-2207",
"id": "CVE-2022-2207",
"initial_release_date": "2023-02-15T03:32:56Z",
"product_status:known_affected": "237",
"product_status:known_not_affected": "26",
"product_status:recommended": "501",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2022-2207",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2022-2207.json",
"version": "45"
}
}
}
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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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