CVE-2022-38013 (GCVE-0-2022-38013)
Vulnerability from cvelistv5 – Published: 2022-09-13 00:00 – Updated: 2026-05-27 13:49
VLAI
EPSS
VEX
Title
.NET Core and Visual Studio Denial of Service Vulnerability
Summary
.NET Core and Visual Studio Denial of Service Vulnerability
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-08-15 14:39 UTC
CWE
- Denial of Service
- CWE-400 - Uncontrolled Resource Consumption
Assigner
References
7 references
| URL | Tags |
|---|---|
| https://msrc.microsoft.com/update-guide/vulnerabi… | vendor-advisory |
| https://portal.msrc.microsoft.com/en-US/security-… | x_transferred |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisoryx_transferred |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisoryx_transferred |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisoryx_transferred |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisoryx_transferred |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisoryx_transferred |
Impacted products
8 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Microsoft | Visual Studio 2022 for Mac version 17.3 |
Affected:
17.3 , < 17.3.5
(custom)
|
guessed | |
| Microsoft | Microsoft Visual Studio 2022 version 17.3 |
Affected:
17.0.0 , < 17.3.4
(custom)
|
guessed | |
| Microsoft | .NET Core 3.1 |
Affected:
3.1 , < 3.1.29
(custom)
|
guessed | |
| Microsoft | .NET 6.0 |
Affected:
6.0.0 , < 6.0.9
(custom)
|
guessed | |
| Microsoft | Microsoft Visual Studio 2019 version 16.11 (includes 16.0 - 16.10) |
Affected:
16.11.0 , < 16.11.19
(custom)
|
guessed | |
| Microsoft | Microsoft Visual Studio 2019 version 16.9 (includes 16.0 - 16.8) |
Affected:
15.0.0 , < 16.9.25
(custom)
|
guessed | |
| Microsoft | Microsoft Visual Studio 2022 version 17.0 |
Affected:
17.0.0 , < 17.0.14
(custom)
|
guessed | |
| Microsoft | Microsoft Visual Studio 2022 version 17.2 |
Affected:
17.2.0 , < 17.2.8
(custom)
|
guessed |
Date Public
2022-09-13 07:00
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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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