FKIE_CVE-2021-20227
Vulnerability from fkie_nvd - Published: 2021-03-23 17:15 - Updated: 2026-06-17 03:33
Severity
Summary
A flaw was found in SQLite's SELECT query functionality (src/select.c). This flaw allows an attacker who is capable of running SQL queries locally on the SQLite database to cause a denial of service or possible code execution by triggering a use-after-free. The highest threat from this vulnerability is to system availability.
References
Impacted products
| Vendor | Product | Version | |
|---|---|---|---|
| sqlite | sqlite | * | |
| oracle | communications_network_charging_and_control | * | |
| oracle | communications_network_charging_and_control | 6.0.1 | |
| oracle | enterprise_manager_for_oracle_database | 13.4.0.0 | |
| oracle | jd_edwards_enterpriseone_tools | * | |
| oracle | mysql_workbench | * | |
| oracle | outside_in_technology | 8.5.5 | |
| oracle | zfs_storage_appliance_kit | 8.8 |
{
"affected": [
{
"affectedData": [
{
"product": "sqlite",
"vendor": "n/a",
"versions": [
{
"status": "affected",
"version": "sqlite 3.34.1"
}
]
}
],
"source": "secalert@redhat.com"
}
],
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"cveTags": [],
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"value": "A flaw was found in SQLite\u0027s SELECT query functionality (src/select.c). This flaw allows an attacker who is capable of running SQL queries locally on the SQLite database to cause a denial of service or possible code execution by triggering a use-after-free. The highest threat from this vulnerability is to system availability."
},
{
"lang": "es",
"value": "Se encontr\u00f3 un fallo en la funcionalidad de consulta SELECT de SQLite (src/select.c).\u0026#xa0;Este fallo permite a un atacante que es capaz de ejecutar consultas SQL localmente en la base de datos SQLite causar una denegaci\u00f3n de servicio o una posible ejecuci\u00f3n de c\u00f3digo desencadenando un uso de la memoria previamente liberada.\u0026#xa0;La mayor amenaza de esta vulnerabilidad es la disponibilidad del sistema"
}
],
"id": "CVE-2021-20227",
"lastModified": "2026-06-17T03:33:30.333",
"metrics": {
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"published": "2021-03-23T17:15:13.747",
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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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