Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:H/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2024-11956 is a medium-severity Injection (CWE-74) vulnerability in Pimcore Pimcore. Its CVSS base score is 5.1 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 45% of CVEs by exploit likelihood; 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 SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2024-11956 is a SQL injection vulnerability (classified as critical, associated with CWE-74 and CWE-89) in Pimcore customer-data-framework versions up to 4.2.0. The issue affects unknown functionality in the file /admin/customermanagementframework/customers/list, where manipulation of the filterDefinition/filter argument triggers the injection.
The vulnerability is exploitable remotely over the network (AV:N) with low attack complexity (AC:L), but requires high privileges (PR:H) and no user interaction (UI:N). Attackers with sufficient access can achieve low impacts on confidentiality, integrity, and availability (C:L/I:L/A:L), corresponding to a CVSS v3.1 base score of 4.7 in an unchanged scope (S:U).
Advisories from Pimcore and VulDB recommend upgrading to version 4.2.1, which addresses the issue, as detailed in the GitHub release notes and security advisory (GHSA-q53r-9hh9-w277).
The exploit has been disclosed publicly and may be used, with the CVE published on 2025-01-28.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-0185
Vulnerability Data
A vulnerability, which was classified as critical, has been found in Pimcore customer-data-framework up to 4.2.0. Affected by this issue is some unknown functionality of the file /admin/customermanagementframework/customers/list. The manipulation of the argument filterDefinition/filter leads to sql injection. The attack…
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may be launched remotely. The exploit has been disclosed to the public and may be used. Upgrading to version 4.2.1 is able to address this issue. It is recommended to upgrade the affected component.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover SQLi flaws before deployment but does not stop their introduction.
SI-10 directly requires validation of information inputs to reject malformed or special-element content before it reaches downstream parsers.
Secure engineering principles require parameterized queries and input sanitization that structurally eliminate SQLi.
System monitoring can identify attempted SQLi exploitation via anomalous queries after the weakness exists.
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 SDLC practices directly require input validation and output encoding that prevent injection flaws.
Training raises developer awareness of SQLi risks and can reduce introduction likelihood (partial) but removes none of the actual coding flaw's risk by itself since technical neutralization is still required.
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.
Security testing in development catches injection vulnerabilities before release.
Early warnings and shared best-practice information help organizations apply the latest remediation techniques against SQL-injection vulnerabilities.
Threat-intelligence feeds that surface new SQL-injection campaigns enable rapid updates to query-construction defenses and detection signatures before exploitation occurs.
Logging supports detection of injection attempts but does not prevent the weakness.
Monitoring activities can identify active injection attacks after they occur.
Secure development life cycle mandates input validation and output encoding that directly prevent injection flaws.