FKIE_CVE-2020-14422
Vulnerability from fkie_nvd - Published: 2020-06-18 14:15 - Updated: 2026-06-17 02:54
Severity
Summary
Lib/ipaddress.py in Python through 3.8.3 improperly computes hash values in the IPv4Interface and IPv6Interface classes, which might allow a remote attacker to cause a denial of service if an application is affected by the performance of a dictionary containing IPv4Interface or IPv6Interface objects, and this attacker can cause many dictionary entries to be created. This is fixed in: v3.5.10, v3.5.10rc1; v3.6.12; v3.7.9; v3.8.4, v3.8.4rc1, v3.8.5, v3.8.6, v3.8.6rc1; v3.9.0, v3.9.0b4, v3.9.0b5, v3.9.0rc1, v3.9.0rc2.
References
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"lang": "en",
"value": "Lib/ipaddress.py in Python through 3.8.3 improperly computes hash values in the IPv4Interface and IPv6Interface classes, which might allow a remote attacker to cause a denial of service if an application is affected by the performance of a dictionary containing IPv4Interface or IPv6Interface objects, and this attacker can cause many dictionary entries to be created. This is fixed in: v3.5.10, v3.5.10rc1; v3.6.12; v3.7.9; v3.8.4, v3.8.4rc1, v3.8.5, v3.8.6, v3.8.6rc1; v3.9.0, v3.9.0b4, v3.9.0b5, v3.9.0rc1, v3.9.0rc2."
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"value": "La biblioteca Lib/ipaddress.py en Python versiones hasta 3.8.3, calcula inapropiadamente los valores de hash en las clases IPv4Interface e IPv6Interface, lo que podr\u00eda permitir a un atacante remoto causar una denegaci\u00f3n de servicio si una aplicaci\u00f3n est\u00e1 afectada por el desempe\u00f1o de un diccionario que contiene objetos de IPv4Interface o IPv6Interface, y este atacante puede causar que muchas entradas de diccionario sean creadas. Esto esta corregido en las versiones: v3.5.10, v3.5.10rc1; v3.6.12; v3.7.9; v3.8.4, v3.8.4rc1, v3.8.5, v3.8.6, v3.8.6rc1; v3.9.0, v3.9.0b4, v3.9.0b5, v3.9.0rc1, v3.9.0rc2"
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"id": "CVE-2020-14422",
"lastModified": "2026-06-17T02:54:44.160",
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"accessVector": "NETWORK",
"authentication": "NONE",
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"baseScore": 4.3,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:M/Au:N/C:N/I:N/A:P",
"version": "2.0"
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"scope": "UNCHANGED",
"userInteraction": "NONE",
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"published": "2020-06-18T14:15:11.047",
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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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