CVE-2024-2088
Nextscripts Social Networks Auto Poster ≤ 4.4.4
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-2088 is a high-severity Exposure of Sensitive Information Through Data Queries (CWE-202) vulnerability in Nextscripts Social Networks Auto Poster. Its CVSS base score is 8.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Steganography (T1001.002); ranked at the 26th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
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-2024-27052
Vulnerability Data
The NextScripts: Social Networks Auto-Poster plugin for WordPress is vulnerable to Sensitive Information Exposure in all versions up to, and including, 4.4.3 via the 'nxs_getExpSettings' function. This makes it possible for authenticated attackers, with subscriber access and above, to extract…
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sensitive data including social network API keys and secrets.
- 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.
Behavior analytics on query activity can detect inference attempts but does not prevent exposure at query time.
Least-privilege query permissions directly limit the data an attacker can request or infer.
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.
Classification helps identify sensitive data that must be protected from inference attacks.
A.5.15 defines governance, policies, requirements and rules for access control (including need-to-know and classification consistency) but does not itself enforce, implement or act on those rules to stop statistical inference from data queries; per the owner's labeling rule, this is a dependency for other controls (e.g. actual access enforcement or DLP) rather than a prevention effect.
A.5.18 governs the provisioning, review, and revocation of authorized access rights; it has no bearing on whether an attacker can statistically infer confidential information from data queries, which is a disclosure weakness unrelated to access-right decisions.
Data masking prevents inference by obscuring sensitive values returned in query results.
DLP can detect and block queries or result sets that risk exposing sensitive information.
Logging query activity supports detection of inference attempts after the fact.