VAR-201705-3521
Vulnerability from variot - Updated: 2025-04-20 23:35In F5 BIG-IP 12.0.0 through 12.1.2, an authenticated attacker may be able to cause an escalation of privileges through a crafted iControl REST connection. plural F5 BIG-IP The product contains vulnerabilities related to authorization, permissions, and access control.Information is obtained, information is altered, and service operation is disrupted (DoS) There is a possibility of being put into a state. F5BIG-IP is a load balancer that uses a variety of allocation algorithms to distribute network requests to available servers in a server cluster. By managing incoming web data traffic and increasing effective network bandwidth, network visitors get as much as possible. The hardware device for the best networking experience. F5BIG-IPiControlREST has a remote privilege elevation vulnerability that allows remote authenticated users to issue a specially crafted iControlREST connection to gain elevated privileges on the target system. F5 BIG-IP LTM, etc. are all products of F5 Company in the United States. LTM is a local traffic manager; APM is a solution that provides secure unified access to business-critical applications and networks. Security flaws exist in several F5 products. The following products and versions are affected: F5 BIG-IP LTM version 12.0.0 to 12.1.2; BIG-IP AAM version 12.0.0 to 12.1.2; BIG-IP AFM version 12.0.0 to 12.1.2; BIG-IP Analytics version 12.0.0 through 12.1.2; BIG-IP APM version 12.0.0 through 12.1.2; BIG-IP ASM version 12.0.0 through 12.1.2; BIG-IP DNS version 12.0.0 to version 12.1.2; BIG-IP Link Controller version 12.0.0 to version 12.1.2; BIG-IP PEM version 12.0.0 to version 12.1.2; BIG-IP WebSafe version 12.0.0 to version 12.1.2
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Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.
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.
Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
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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.