CVE-2024-53907 (GCVE-0-2024-53907)
Vulnerability from cvelistv5 – Published: 2024-12-06 00:00 – Updated: 2024-12-31 18:03
VLAI
EPSS
VEX
Summary
An issue was discovered in Django 5.1 before 5.1.4, 5.0 before 5.0.10, and 4.2 before 4.2.17. The strip_tags() method and striptags template filter are subject to a potential denial-of-service attack via certain inputs containing large sequences of nested incomplete HTML entities.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-12-06 16:22 UTC
CWE
- n/a
- CWE-770 - Allocation of Resources Without Limits or Throttling
Assigner
References
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| djangoproject | django |
Affected:
5.1 , < 5.1.4
(custom)
Affected: 5.0 , < 5.0.10 (custom) Affected: 4.2 , < 4.2.17 (custom) cpe:2.3:a:djangoproject:django:*:*:*:*:*:*:*:* |
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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.
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.
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