VAR-201705-3164
Vulnerability from variot - Updated: 2025-04-20 23:35A stored XSS (Cross-Site-Scripting) vulnerability in Fortinet FortiOS allows attackers to execute unauthorized code or commands via the policy global-label parameter. FortiOS is prone to an HTML-injection vulnerability because it fails to properly sanitize user-supplied input. Successful exploits will result in the execution of arbitrary attacker-supplied HTML and script code in the context of the affected application, potentially allowing the attacker to steal cookie-based authentication credentials or control how the page is rendered to the user. Other attacks are also possible. Fortinet FortiOS is a set of security operating systems developed by Fortinet Corporation for the FortiGate network security platform. The system provides users with various security functions such as firewall, anti-virus, IPSec/SSL VPN, Web content filtering and anti-spam. A cross-site scripting vulnerability exists in Fortinet FortiOS
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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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