VAR-201407-0047
Vulnerability from variot - Updated: 2025-04-13 23:22Multiple cross-site scripting (XSS) vulnerabilities in FortiGuard FortiWeb 5.0.x, 5.1.x, and 5.2.x before 5.2.1 allow remote attackers to inject arbitrary web script or HTML via unspecified vectors to (1) user/ldap_user/check_dlg or (2) user/radius_user/check_dlg. Fortinet Fortiweb is prone to multiple cross-site scripting vulnerabilities because it fails to properly sanitize user-supplied input. An attacker may leverage these issues to execute arbitrary script code in the browser of an unsuspecting user in the context of the affected site. This can allow the attacker to steal cookie-based authentication credentials and launch other attacks. Fortinet Fortiweb 5.0.x, 5.1.x and 5.2.0 are vulnerable. Fortinet FortiGuard FortiWeb is a web application layer firewall developed by Fortinet, which can block threats such as cross-site scripting, SQL injection, cookie poisoning, schema poisoning, etc. Sensitive database content. A cross-site scripting vulnerability exists in the user/ldap_user/check_dlg and user/radius_user/check_dlg URIs of Fortinet FortiGuard FortiWeb 5.0.x to 5.2.0
Show details on source website{
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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.
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- 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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