CVE-2025-59428 (GCVE-0-2025-59428)
Vulnerability from cvelistv5 – Published: 2025-10-14 14:38 – Updated: 2025-10-14 16:03
VLAI
EPSS
VEX
Title
EspoCRM allows arbitrary user creation via stored SVG injection and CSRF
Summary
EspoCRM is an open source customer relationship management application. In versions before 9.1.9, a vulnerability allows arbitrary user creation, including administrative accounts, through a combination of stored SVG injection and lack of CSRF protection. An attacker with Knowledge Base edit permissions can embed a malicious SVG element containing a link in the body field of an article. When an authenticated user clicks the malicious link, they are redirected to an attacker-controlled HTML page that executes a CSRF request against the api/v1/User endpoint. If the victim is prompted for and enters their credentials, an attacker-controlled account is created with privileges determined by the CSRF payload. This issue has been patched in version 9.1.9.
Severity
5.4 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-10-14 16:03 UTC
CWE
- CWE-352 - Cross-Site Request Forgery (CSRF)
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://github.com/espocrm/espocrm/security/advis… | x_refsource_CONFIRM |
Impacted products
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
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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.
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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.