CVE-2014-3995 (GCVE-0-2014-3995)
Vulnerability from cvelistv5 – Published: 2014-06-16 18:00 – Updated: 2024-08-06 11:04
VLAI
EPSS
VEX
Summary
Cross-site scripting (XSS) vulnerability in gravatars/templatetags/gravatars.py in Djblets before 0.7.30 and 0.8.x before 0.8.3 for Django allows remote attackers to inject arbitrary web script or HTML via a user display name.
Severity
No CVSS data available.
CWE
- n/a
Assigner
References
6 references
| URL | Tags |
|---|---|
| http://seclists.org/oss-sec/2014/q2/494 | mailing-listx_refsource_MLIST |
| http://seclists.org/oss-sec/2014/q2/498 | mailing-listx_refsource_MLIST |
| https://github.com/djblets/djblets/commit/77ac646… | x_refsource_CONFIRM |
| http://secunia.com/advisories/58691 | third-party-advisoryx_refsource_SECUNIA |
| https://github.com/djblets/djblets/commit/e2c7911… | x_refsource_CONFIRM |
| https://github.com/djblets/djblets/commit/50000d0… | x_refsource_CONFIRM |
Date Public
2014-06-06 00:00
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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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