CVE-2024-1287
Strangerstudios Paid Memberships Pro ≤ 1.2.6
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2024-1287 is a medium-severity Exposure of Sensitive Information Through Data Queries (CWE-202) vulnerability in Strangerstudios Paid Memberships Pro. Its CVSS base score is 6.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Information Repositories (T1213); ranked at the 42th 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 AC-23 (Data Mining Protection) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-17048
Vulnerability Data
The pmpro-member-directory WordPress plugin before 1.2.6 does not prevent users with at least the contributor role from leaking other users' sensitive information, including password hashes via an SQLi vector.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
AC-23 directly requires mechanisms to protect against unauthorized data mining and inference from statistical queries that would expose sensitive information.
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.
Least-privilege query permissions directly limit the data an attacker can request or infer.
Behavior analytics on query activity can detect inference attempts but does not prevent exposure at query time.
Protecting data-in-use reduces what remains available for inference via queries.
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.
Access control limits who can run queries that could expose sensitive information via inference.
Granular access rights reduce the ability of users to craft inference queries.
Data masking prevents inference by obscuring sensitive values returned in query results.
Information access restriction directly limits query scope that could lead to inference.
Classification helps identify sensitive data that must be protected from inference attacks.
DLP can detect and block queries or result sets that risk exposing sensitive information.