CVE-2023-43795 (GCVE-0-2023-43795)
Vulnerability from cvelistv5 – Published: 2023-10-24 22:14 – Updated: 2024-09-17 14:15Title
WPS Server Side Request Forgery in GeoServer
Summary
GeoServer is an open source software server written in Java that allows users to share and edit geospatial data. The OGC Web Processing Service (WPS) specification is designed to process information from any server using GET and POST requests. This presents the opportunity for Server Side Request Forgery. This vulnerability has been patched in version 2.22.5 and 2.23.2.
Severity
8.6 (High)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-09-11 13:52 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/geoserver/geoserver/security/a… | x_refsource_CONFIRM |
Impacted products
Previdian
Known Exploited Vulnerability - GCVE BCP-07 Compliant
KEV entry ID: b06a3bee-22fa-4173-b262-c6028422525b
Exploited: Yes
Timestamps
First Seen: 2025-04-26
Asserted: 2025-04-26
Scope
Notes: WPS Server Side Request Forgery in GeoServer | Affected: Geoserver / geoserver | CVSS: 8.6 (HIGH) | EPSS: 0.67715 | Used in malware: unknown | Not yet in CISA KEV: True
Evidence
Type: Public Report
Signal: Successful Exploitation
Confidence: 70%
Source: previdian
Details
| Feed | Previdian (previdian.com) |
|---|---|
| Title | WPS Server Side Request Forgery in GeoServer |
| Cve Id | CVE-2023-43795 |
| Vendor | Geoserver |
| Ghsa Id | GHSA-5PR3-M5HM-9956 |
| Product | geoserver |
| Added Date | 2025-04-26T00:00:00.000Z |
| Cvss Score | 8.6 |
| Epss Score | 0.67715 |
| Previous Ids | |
| Cvss Severity | HIGH |
| Virtual Patch | False |
| Cvss Estimated | False |
| Epss Percentile | 0.99297 |
| Used In Malware | unknown |
| Vulnerability Id | CVE-2023-43795 |
| Ahead Of Cisa Kev | None |
| Not Yet In Cisa Kev | True |
References
Created: 2026-10-02 09:10 CEST
| Updated: 2026-10-02 09:10 CEST
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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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