FKIE_CVE-2015-5964
Vulnerability from fkie_nvd - Published: 2015-08-24 14:59 - Updated: 2026-06-17 00:30
Severity
Summary
The (1) contrib.sessions.backends.base.SessionBase.flush and (2) cache_db.SessionStore.flush functions in Django 1.7.x before 1.7.10, 1.4.x before 1.4.22, and possibly other versions create empty sessions in certain circumstances, which allows remote attackers to cause a denial of service (session store consumption) via unspecified vectors.
References
Impacted products
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"cveTags": [],
"descriptions": [
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"lang": "en",
"value": "The (1) contrib.sessions.backends.base.SessionBase.flush and (2) cache_db.SessionStore.flush functions in Django 1.7.x before 1.7.10, 1.4.x before 1.4.22, and possibly other versions create empty sessions in certain circumstances, which allows remote attackers to cause a denial of service (session store consumption) via unspecified vectors."
},
{
"lang": "es",
"value": "Vulnerabilidad en las funciones (1) contrib.sessions.backends.base.SessionBase.flush y (2) cache_db.SessionStore.flush en Django 1.7.x en versiones anteriores a 1.7.10, 1.4.x en versiones anteriores a 1.4.22 y posiblemente en otras versiones, crea sesiones vac\u00edas en ciertas circunstancias, que permite a atacantes remotos causar una denegaci\u00f3n de servicio (consumo de almac\u00e9n de sesi\u00f3n) a trav\u00e9s de vectores no especificados."
}
],
"id": "CVE-2015-5964",
"lastModified": "2026-06-17T00:30:11.870",
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"baseSeverity": "MEDIUM",
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"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "PARTIAL",
"baseScore": 5.0,
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:N/C:N/I:N/A:P",
"version": "2.0"
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"exploitabilityScore": 10.0,
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"source": "nvd@nist.gov",
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"published": "2015-08-24T14:59:09.837",
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"vulnStatus": "Modified",
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"lang": "en",
"value": "CWE-399"
}
],
"source": "nvd@nist.gov",
"type": "Primary"
}
]
}
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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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