GSD-2024-24680
Vulnerability from gsd - Updated: 2024-01-27 06:03Details
An issue was discovered in Django 3.2 before 3.2.24, 4.2 before 4.2.10, and Django 5.0 before 5.0.2. The intcomma template filter was subject to a potential denial-of-service attack when used with very long strings.
Aliases
{
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2024-24680"
],
"details": "An issue was discovered in Django 3.2 before 3.2.24, 4.2 before 4.2.10, and Django 5.0 before 5.0.2. The intcomma template filter was subject to a potential denial-of-service attack when used with very long strings.",
"id": "GSD-2024-24680",
"modified": "2024-01-27T06:03:16.307108Z",
"schema_version": "1.4.0"
}
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"url": "https://www.djangoproject.com/weblog/2024/feb/06/security-releases/"
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"cve": {
"configurations": [
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"nodes": [
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"value": "An issue was discovered in Django 3.2 before 3.2.24, 4.2 before 4.2.10, and Django 5.0 before 5.0.2. The intcomma template filter was subject to a potential denial-of-service attack when used with very long strings."
},
{
"lang": "es",
"value": "Se descubri\u00f3 un problema en Django 3.2 anterior a 3.2.24, 4.2 anterior a 4.2.10 y Django 5.0 anterior a 5.0.2. El filtro de plantilla intcomma estaba sujeto a un posible ataque de denegaci\u00f3n de servicio cuando se utilizaba con cadenas muy largas."
}
],
"id": "CVE-2024-24680",
"lastModified": "2024-04-20T03:15:06.820",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
},
"exploitabilityScore": 3.9,
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"source": "nvd@nist.gov",
"type": "Primary"
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"published": "2024-02-06T22:16:15.470",
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}
}
}
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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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