Raw vector
CVSS:3.1/AV:A/AC:L/PR:N/UI:R/S:U/C:L/I:L/A:LSummary
CVE-2025-45250 is a medium-severity SSRF (CWE-918) vulnerability in Mrdoc Mrdoc. Its CVSS base score is 5.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Network Service Discovery (T1046); ranked at the 18th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to SI-10 (Information Input Validation) and AC-4 (Information Flow Enforcement) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-13633
Vulnerability Data
MrDoc v0.95 and before is vulnerable to Server-Side Request Forgery (SSRF) in the validate_url function of the app_doc/utils.py file.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
SSRF vulnerability enables exploitation of public-facing web application (T1190), internal network service discovery via server-side requests (T1046), and use of the vulnerable application as an internal proxy to access or pivot to internal systems (T1090.001).
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly requires validation of URL inputs in functions like validate_url to reject malformed or unauthorized destination addresses that enable SSRF.
Enforces boundary controls on outbound network requests, limiting the server’s ability to reach arbitrary internal or external resources via the vulnerable validate_url path.
Implements information flow policies that restrict which destinations a server-side component may contact, blocking the unauthorized request flows exploited by this SSRF flaw.
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.