Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:L/A:LSummary
CVE-2023-43795 is a high-severity SSRF (CWE-918) vulnerability in Osgeo Geoserver. Its CVSS base score is 8.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 0.7% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
GeoServer is an open source Java server for sharing and editing geospatial data that is affected by a Server Side Request Forgery vulnerability in its OGC Web Processing Service implementation. The WPS specification accepts arbitrary GET and POST requests, enabling the flaw tracked as CWE-918 and carrying a CVSS 3.1 score of 8.6.
An unauthenticated remote attacker can send crafted requests to induce the server into issuing outbound connections to internal or external resources, resulting in disclosure of sensitive data together with limited integrity and availability effects.
The published GitHub security advisory states that the issue has been fixed in GeoServer releases 2.22.5 and 2.23.2.
The associated EPSS score stands at 0.8949 with no material rise after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-2659
Vulnerability Data
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…
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presents the opportunity for Server Side Request Forgery. This vulnerability has been patched in version 2.22.5 and 2.23.2.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Penetration testing attempts server-side requests to internal resources, identifying SSRF weaknesses for remediation.
Outbound connections to external resources can be monitored and limited at the boundary, reducing SSRF impact.
Validates server-side URLs and resource references to block SSRF attempts.
Detects server-side request forgery through monitoring of unexpected outbound connections.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure development practices directly include input validation and destination allow-listing that prevent SSRF.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.