CVE-2023-21528 (GCVE-0-2023-21528)
Vulnerability from cvelistv5 – Published: 2023-02-14 19:32 – Updated: 2026-08-19 16:26
VLAI
EPSS
VEX
Title
Microsoft SQL Server Remote Code Execution Vulnerability
Summary
Microsoft SQL Server Remote Code Execution Vulnerability
Severity
CWE
- CWE-122 - Heap-based Buffer Overflow
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://msrc.microsoft.com/update-guide/vulnerabi… | vendor-advisorypatch |
Impacted products
13 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Microsoft | Microsoft SQL Server 2008 R2 Service Pack 3 (QFE) |
Affected:
10.0.0 , < 10.50.6785.2
(custom)
|
guessed | |
| Microsoft | Microsoft SQL Server 2008 Service Pack 4 (QFE) |
Affected:
10.0.0 , < 10.0.6814.4
(custom)
|
guessed | |
| Microsoft | Microsoft SQL Server 2012 for x64-based Systems Service Pack 4 (QFE) |
Affected:
11.0.0 , < 11.0.7512.11
(custom)
|
guessed | |
| Microsoft | Microsoft SQL Server 2012 Service Pack 4 (QFE) |
Affected:
11.0.0 , < 11.0.7512.11
(custom)
|
guessed | |
| Microsoft | Microsoft SQL Server 2014 Service Pack 3 (CU 4) |
Affected:
12.0.0 , < 12.0.6174.8
(custom)
|
guessed | |
| Microsoft | Microsoft SQL Server 2014 Service Pack 3 (GDR) |
Affected:
12.0.0 , < 12.0.6444.4
(custom)
|
guessed | |
| Microsoft | Microsoft SQL Server 2016 Service Pack 3 (GDR) |
Affected:
13.0.0 , < 13.0.6430.49
(custom)
|
guessed | |
| Microsoft | Microsoft SQL Server 2016 Service Pack 3 Azure Connect Feature Pack |
Affected:
13.0.0 , < 13.0.7024.30
(custom)
|
guessed | |
| Microsoft | Microsoft SQL Server 2017 (CU 31) |
Affected:
14.0.0 , < 14.0.3460.9
(custom)
|
guessed | |
| Microsoft | Microsoft SQL Server 2017 (GDR) |
Affected:
14.0.0 , < 14.0.2047.8
(custom)
|
guessed | |
| Microsoft | Microsoft SQL Server 2019 (CU 18) |
Affected:
15.0.0 , < 15.0.4280.7
(custom)
|
guessed | |
| Microsoft | Microsoft SQL Server 2019 (GDR) |
Affected:
15.0.0 , < 15.0.2101.7
(custom)
|
guessed | |
| Microsoft | Microsoft SQL Server 2022 (GDR) |
Affected:
16.0.0 , < 16.0.1050.5
(custom)
|
guessed |
Date Public
2023-02-14 08:00
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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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